Circularly polarised light will revolutionise emerging technologies, including encrypted light-based communications, quantum computing, bioimaging and multi-channel data processing. In order to make use of these remarkable opportunities, high performance photodetectors that can accurately differentiate between left- and right-handed circularly polarised light are desperately needed. Whilst this potential has resulted in considerable research interest in chiral materials and circularly polarised photodetecting devices, their translation into real-world technologies is limited by non-standardised reporting and testing protocols. This mini-review provides an accessible introduction into the working principles of circularly polarised photodetectors and a comprehensive overview of the performance metrics of state-of-the-art devices. We propose a rigorous device characterisation procedure that will allow for standardised evaluation of novel devices, which we hope will accelerate research and investment in this area. Here we provide a framework of standardised apparatus and tests which enable rigorous characterisation of the performance of photodetectors sensitive to circular polarisation, enabling meaningful comparisons between devices reported in literature. Organic based photodetectors are commonly referred to as OPDs; whilst we will use this term here, we note that most of the principles discussed and some of the examples provided involve hybrid or perovskite-based devices. Several OPD device architectures have been considered and strategies to improve device performance  are emerging.12 The circularly selective optical response of chiral functional materials makes them an exciting prospect for direct detection of CP light without the need for external optics.13 To this end, a variety of organic and organic–inorganic chiral materials have been developed.14,15 Due to the strong focus of recent studies on maximising the CP selectivity of detectors, conventional non-CP figures of merit have been largely sidelined. This has led to poor overall performances of devices reported to date, limiting their suitability for widespread application of CP OPDs in real-world technologies. Furthermore, the rapidly growing highly interdisciplinary nature of the research field has resulted in a variety of characterisation protocols, and incomplete reporting of figures of merit, rendering meaningful comparisons between different materials and device architectures a challenge. The development and use of common standards, which were essential for the commercialisation of photovoltaics, photodetectors and light emitting diodes, are desperately needed.Owing to their tuneable detection wavelengths, simple processing requirements and compatibility with low-cost manufacturing processes, photodetectors based on organic and hybrid organic–inorganic semiconductors have attracted intense research interest in recent years.13–16 Here we carefully set out the performance metrics and instrumentation essential to characterise device performance and CP selectivity of OPDs based on chiral functional materials. We hope that this tutorial review serves to advance the field, such that CP OPDs with device performances and sensitivities compatible with the demands of real-world sensing can be realised.Here we consider the experimental measurements required to characterise CP OPDs rigorously and accurately. We propose a standardised means of reporting their key figures of merit, including Linear Dynamic Range (LDR), Bandwidth and Responsivity. As recent reviews have focussed on the design of materials capable of detecting circularly polarised light and the characterisation of their chiroptical response, we do not provide a further comprehensive review of those aspects here. Instead, we direct readers to several recent and detailed review articles.We first provide a brief introduction to the materials systems of current state-of-the-art CP OPDs. We then describe the operating principles of the three most common photodetectors  and outline the standardised figures of merit of these devices. We conclude by outlining strategies to make testing protocols more robust, which should result in standardised evaluation and reporting of device performance.2.etc.There are several materials characteristics which are desirable in the pursuit of high-performance photodetectors, including large optical absorption coefficients and long recombination times. Advances in molecular design have improved the spectral sensitivity of and charge transport in non-chiral organic and hybrid organic–inorganic materials, although some of these strategies do not transfer cleanly to chiral materials due to inherent differences in molecular shape, packing, εr ∼ 3.5 vs. εr ≥ 10), which results in the formation of Coulombically bound electron–hole pairs (excitons) under optical excitation. As thermal dissociation is highly improbable in a low dielectric constant medium, the generation of free charges in organic semiconductors typically requires the intermixing of two semiconductors with suitable energetics to yield a heterojunction between an electron donating and an electron accepting component.17 The electron accepting or electron donating character of the organic semiconductor is controlled through chemical design, for example by incorporating electron withdrawing or donating groups into the molecular structure.Organic semiconductors display much lower dielectric constants than their inorganic counterparts , such as IDTBR, have emerged as alternatives to fullerene-based acceptors with broader spectral photosensitivity and improved long-term morphological stability whilst retaining good charge-transport properties.18,19 Most NFAs make use of push-pull hybridisation, which allows them to absorb strongly in the visible and NIR region of the solar spectrum, achieving higher voltage outputs compared to fullerene-based alternatives.18,19Until recently, the most common selection of non-chiral materials in OPDs comprised homopolymer donors, such as P3HT, or push–pull polymers , in combination with fullerene-based acceptors see .18,19 Ree.g., perovskites) are popular candidates for photodetection, owing to their remarkable charge carrier mobilities and high optical absorption. Further, perovskites have large dielectric constants (εr ∼ 20–50), which circumvents the need for heterojunctions to efficiently generate free charges.20Alongside organic semiconductors, hybrid organic–inorganic materials  and right-handed (RH) light. It should be noted that the measured absorption expression does not account for reflection losses occurring at the front and back surfaces of thin films. It is possible to mitigate for these reflection losses by calculating the corrected absorbance of the photoactive layer of thickness d:22α can be determined by measuring the apparent absorbance (Absmeas) as a function of film thickness (d) and calculated using the following expression:R)2 accounts for reflection losses of a free-standing film. This allows a corrected absorbance g-factor (gcorr) to be determined:gabs (gph) within a CP photodetecting device describes the differential photocurrent (Iph) generated in CP illumination:IL and IR are the photocurrents Iph  under LH and RH light. Note that the analogous definitions of responsivity dissymmetry (gR) and external quantum efficiency dissymmetry (gEQE) used in literature are mathematically identical to gph for the same intensity and wavelength of incident light, and as such will not be defined here  and magnetic (m) transition dipole moments.15 For most molecular systems, |m| ≪ |µ|, which results in vanishingly small dissymmetry factors (gabs ∼ 10−4–10−3). Strategies have emerged to increase gabs, including rational molecular design, the formation of supramolecular chiral structures in which considerable gabs can be achieved through circular-selective scattering, and coupling between excited states on nearby chromophores.23–29 For example, in the solid-state, systems based on achiral π-conjugated polymers blended with chiral small molecule additives can give rise to very large gabs (>0.1).30 However, these approaches can come at a cost: the twisted molecular structures required in these approaches can compromise molecular packing and impede charge transport.From a molecular perspective, 3.The photophysical mechanisms that underpin photodetection depend on the device architecture. Here we introduce the most common device architectures , discussIph), which is given by:q is the electron charge, η is the photocurrent external quantum efficiency, Popt is the incident optical power, hν is the incident photon energy, μ is the majority carrier mobility, τRec is the electron–hole recombination time, V is the applied voltage between the terminals, and L is the photoconductor electrode spacing.11,31Ip is the primary photocurrent  and Ga is the device gain, given by:τRec) is longer than the transit time for carriers to move between the two electrodes (τTrans).11,31 Small τTrans requires small electrode spacing, L, which can reduce the photoactive area of the photoconductor, unless the device width is increased to compensate. In this scenario, photogenerated carriers can traverse the active layer for several cycles before recombining, with the average number of cycles equivalent to Ga  to exceed 100%.Photoconductors are two terminal devices in which the conductivity of the active layer increases upon illumination. Optical excitation results in the generation of charge carriers and flow of a photocurrent (nt to Ga . This en2 V−1 s−1) and long carrier lifetimes (10−6 s), which enables significant Ga.20,32–34 Carriers with long τRec persist once the light is switched off, which means that these devices can be slow to respond and have low bandwidths. Chiral perovskite photoconductors generally suffer from poor CP selectivity in absorption (gabs) owing to weak intrinsic chiroptical activity, however a number of recent examples have demonstrated that reducing the dimensionality of the perovskite structure (from quasi-2D to 2D or from 2D to 1D) can enhance gabs by introducing greater asymmetry and helicity into the perovskite crystal structure.35–37 Even perovskites which demonstrate poor gabs can generate considerable gph, for example naphthylethylamine based quasi-2D perovskite . As such, a compromise exists between strong light absorption and efficient exciton harvesting. This has resulted in the development of various photodiode architectures, including bilayer devices, interpenetrating donor-acceptor networks (bulk heterojunctions), and tandem devices.et al demonstrated heterojunction CP OPDs with photocurrent dissymmetry (gPh) ∼ 0.1 by blending achiral polymer, P3CT, with a chiral small molecule additive, 1,1′-binaphthyl polymer (F8T2): chiral small molecule (1-aza[6]helicene) additive donor layer, and exciton dissociation takes place at the donor–acceptor (C60) interface  is above or below the ‘threshold’ voltage (VTh) of the device. Irradiation of p-type  organic phototransistors results in the flow of photogenerated holes to the drain electrode and photogenerated electrons flow to the source. When VG < VTh, phototransistors behave as photoconductors, with photocurrent given by VG > VTh) photogenerated holes contribute to the measured Iph, whilst photogenerated electrons serve to lower the injection barrier (and |VTh|), which increases Iph further:A is a proportionality constant, kT/q is the thermal voltage, η is the photogeneration quantum efficiency, hc/λ is the photon energy and Ipd is the dark current for minority carriers.Phototransistors are three-electrode  devices in which the channel conductivity and charge injection barrier are modulated by external illumination.gph ∼ 1).47–49 For our own studies of phototransistors based on the chiral fullerene bis-PCBM  and chiral polymeric systems (polyfluorene-based (co-)polymers) have been assessed in CP selective phototransistors, achieving very high photocurrent dissymmetry factors  but have performance metrics comparable with state-of-the-art OPDs. For example, in optical communications, a high-speed demodulation system requires a large response to the signal, a broad bandwidth to accommodate variations and minimal background noise. Here we look to establish the key parameters used to evaluate performance of OPDs and propose standard testing protocols that can ensure the accurate, non-biased evaluation of their photoresponse. We have summarised the performance metrics for CP OPDs reported so far in To be of any practical use CP OPDs must not only have impressive selectivity to CP light (high R), which quantifies the ability of a photodetector to convert light into an electrical current for an incident optical power:Popt is the incident optical power, e is the elementary charge, h is Planck's constant, c is the speed of light and λ is the wavelength of the incident light. Whilst photoconductor gain can result in EQEs of over 100%, the EQE of photodiodes is limited to 100%, which leads to small electrical signals when exposed to weak light. Remarkably, the responsivities of 2D chiral perovskite nanowire photodetectors (R = 47 A W−1 at 505 nm) have already exceeded the responsivities of silicon-based devices (R = 0.6 A W−1 at 930 nm).62,68External quantum efficiency (EQE) describes the ratio of the number extracted charges to the number of incident photons at a particular wavelength, for a given applied bias. EQE is closely related to the responsivity  and is desirable for machine vision applications where images may contain both intense bright spots and low light regions which must be accurately measured. Our achiral polyfluorene-based (co-)polymer: chiral small molecule (1-aza[6]helicene) photodiodes achieve an LDR of ∼80 decibels, which is considerably higher than that of InGaAs (∼66 dB) and rivals both silicon (120 dB) and non-chiral OPDs (∼100 dB).30,69Although an important figure of merit for OPDs, LDR is rarely reported in CP OPD literature see . Large LB) or the rise and fall times  of the photodetector.The response speed of a photodetector quantifies the time taken for the photocurrent of a photodetector to change in response to variations in incident light intensity. This establishes a limit on the speed of optical phenomena which can be detected by a given photodetector and the rate of data transfer in an optical communication system. Response speed can be quantified by measuring the bandwidth  or reduces the capacitance of the device (e.g. reducing the electrode area and reducing the photoactive area of the device) will reduce the characteristic RC time. In general, devices that exploit photoconductive gain have slower response times, which limits data transfer rates in optical communication systems. Whilst video applications require B > 10 kHz, frequencies from 100 GHz to THz are particularly promising for next-generation communications because of unused bands of the spectrum, and are essential for technological applications such as the Internet of Things (IoT), autonomous driving, and augmented reality.70The bandwidth  measurestr) is the time taken for the device response to rise from 10% to 90% of its maximum value and the fall time (tf) is the time taken for the device response to fall from 90% to 10% of its maximum value  the high hole mobility of MAPbI3, (ii) the high electron mobility of F8IC and (iii) the suppression of RC delay times due to the reduced active area of these devices . The use of chiral active layers with high charge carrier mobilities and devices with small active areas should be further explored to increase the speed of CP selective detectors to be compatible with communication applications.To date, the fastest organic CP selective photodetectors are our own photodiodes based on chiral small molecule (1-aza[6]helicene) – achiral polymer (F8T2) blends, with a bandwidth of 56 kHz see .30 WhileF8IC see .72 The fRon/off defines the ratio between the device drain current for the device in the on- and off-state for phototransistors:Ron/off value is desirable as this indicates that a low driving voltage is required to achieve a good signal-to-noise ratio.17 This reduces the energy consumption of circuits based on organic transistors and enables their use in low power applications such as IoT devices.On–off ratio 7, exceeding the on–off ratios of some inorganic phototransistors.52,73To date, the highest recorded on–off ratio for a CP selective phototransistor is over 10et al.74 The minimum light power that can be detected is quantified by the noise equivalent power (NEP):B is the electrical bandwidth. The specific detectivity (D*) is the inverse of NEP normalised by the square root of the active area, and can be used to compare the detectivity of devices with different active areas:D*, yet a comprehensive understanding of the intrinsic and external sources is currently lacking.The dark current describes the flow of charge carriers through a photodetector in the dark, whilst the noise current describes random fluctuations in the dark current. Several mechanisms can generate the noise current inside organic photodetectors, which have been summarised by Hirsch 13 Jones for a phototransistor based on an organic small molecule single crystal, relative to a value of 2.3 × 1011 Jones for a typical Si photodiode.52,75The largest detectivity for a CP selective photodetector was reported as 2.9 × 10The operational lifetime of a photodetector represents time for which the photoconductor responsivity remains approximately constant under fixed bias and illumination conditions. For CP selective photodetectors, this must also include the stability of the dissymmetry of photocurrent over time.76,77 This is particularly common in perovskite devices.77Photodetectors typically undergo an initial rapid degradation in device responsivity, referred to as “burn-in”, followed by a slower, more stable decay period (post-burn-in), which must be accounted for in the measurement of operational lifetime.37To the best knowledge of the authors, only one report of the operational lifetime of a CP selective photodetector has been published. This device was a quasi-2D perovskite photoconductor with a reported operational lifetime of one month.5.78 and, separately, the chiroptical activity of chiral materials,79 here we summarise our own best practice for the complete characterisation of CP OPDs.The fast-paced interdisciplinary nature of photodetector research has resulted in incomplete reporting of figures of merit see , which lgabs of a solution or film are measured using a CD spectrometer. For solid state samples, CD spectra should be collected with the sample in various orientations . This allows for the identification of artefacts due to linear dichroism and linear birefringence, which would appear as a shift or inversion of the CD spectrum on rotation or flipping the side of the sample facing the light source.79 Linear artefacts may also be identified through the use of Mueller Matrix Spectroscopic Ellipsometry and Mueller Matrix Polarimetry Imaging, which are capable of resolving the circular dichroism, circular birefringence, linear dichroism and linear birefringence spectra for a sample simultaneously.79,80The circular dichroism (CD) and Responsivity and EQE are measured using the equipment shown in 81 The two EQE spectra of the device under L-CPL and R-CPL can then be used to calculate a photocurrent dissymmetry spectrum.By placing a linear polariser and Fresnel rhomb (a broadband quarter-waveplate) in the path of the chopped monochromatic beam, EQE and responsivity can be measured under circularly polarised light in the wavelength range of 400–1550 nm.via the calibrated photodiode photocurrent) are monitored using the source measure unit. The intensity of the incident light is steadily increased until the device under test deviates from a linear relationship between photocurrent and incident light intensity. The minimum and maximum photocurrents in this linear region are then applied to The LDR of a photodetector is measured using the setup shown in B is measured using the setup shown in f) of the light source is gradually increased until the peak-to-peak voltage of the photodetector signal (Vsin(f)) satisfies the 3 dB condition:Vcw is the continuous wave response of the photodetector to an unmodulated light source with an intensity equal to the peak intensity of the sinusoidally varying light source. This is the “Bandwidth” or “3 dB cut-off frequency” of the device.Detector tr and tf of a photodetector can be extracted using the same setup which is used for the measurement of B. The photodetector is illuminated using a square wave modulated light source with a tuneable switching frequency. The frequency of modulation should be selected so that the device under test produces an oscilloscope signal of the form shown in tr and tf once a signal of this form is acquired. tr and tf should not be extracted using the current measurement of a digital source measure unit, as the measurement sampling frequency is often inadequate for accurate measurements of response times below 10 ms.The B, tr and tf, it is essential that the light source, modulation source, current preamplifier and oscilloscope have electrical bandwidths greater than the bandwidth of the photodetector under test, otherwise the lowest bandwidth component of the testing apparatus will be measured instead of the true bandwidth or response times of the device. For example, if the bandwidth of a photodiode with a −3 dB cut-off frequency of 100 kHz is measured using a current preamplifier with a bandwidth of 10 kHz, an incorrect bandwidth of approximately 10 kHz or less will be measured.In the measurement of et al. and Wang et al., defining the operational lifetime as the time required for the responsivity or photocurrent dissymmetry factor of a device to fall to 80% of their post-burn-in values, whichever comes first.76,77The operational lifetimes of both CP and non-CP photodetectors have been measured using several different methods in the literature. To standardise this measurement, we suggest a similar approach to that taken by Kielar gph as a function of device operation time, we propose that the operational lifetime of a device should be measured under pulsed CP illumination, alternating between L-CP illumination, dark conditions, R-CP illumination, and dark conditions repeatedly. This can be achieved using the setup shown in To measure responsivity and Idark of a photodetector is measured as simply the current flow through the photodetector under dark conditions.The The NEP of a photodetector can be determined by measuring the voltage output of a current preamplifier connected to the device under test in dark conditions using a lock-in amplifier. Most modern digital lock-in amplifiers can provide the noise spectral density of the input signal which, when divided by the responsivity of the detector at a given wavelength, yields the noise equivalent power at that wavelength. This can, in turn, be used to calculate the device detectivity using 6.e.g., linear dynamic range, bandwidth, detectivity) is rarely provided, which limits their application in real-world devices. In this tutorial review we have summarised the most important figures of merit for circularly polarised organic photodetectors and provided detailed descriptions of how to measure them.Circularly polarised organic photodetectors will facilitate future technologies based on image detection, chemical sensing, encrypted communications, and environmental monitoring. The diversity of accessible chiral functional materials that absorb circularly polarised light introduces a wide range of new possibilities, which has inspired considerable research interest in both academia and industry. Whilst reported devices often show impressive chiroptical responses, their device performances are often inferior when compared to non-chiral photodetectors. Further, complete and rigorous characterisation of the critical photodetector parameters  are essential for high-performance devices. Here, chiral organic–inorganic systems  and supramolecular assemblies are the most attractive candidates. Finally, exciton dissociation and the efficient extraction of charge carriers will require the use of multi-layer devices and optimised heterojunction structures with intermixed donor and acceptor domains.Innovations in molecular design, novel device architectures and stronger collaborations between materials chemists and device physicists will advance the field. Optical sensing from the ultraviolet to the infrared is essential for a variety of industrial and scientific applications. To this end, chiral functional materials with photoresponses that span the full range from the UV to the NIR will likely show promise. Chiral materials with outstanding electronic properties (We hope that this review will help to advance this exciting and growing field and inform the design of novel materials/devices with outstanding chiroptical properties and device performance.There are no conflicts to declare.TC-010-D2TC01224C-s001


The recognized limitations of the Red Flag Index include not being subjected to external validation, and the use of a patient’s recollection of symptoms, which is subject to bias. In this issue of CC360, the architects of the IBD-REFER report on their effort to create an improved tool based upon both history and laboratory criteria for the identification of IBD in adults and children. Successfully employing both the Delphi method and multiple regression with sensitivity analysis, the IBD-REFER criteria demonstrated excellent operating characteristics in the internal, but also notably, in the external validation cohort, with sensitivity/specificity in adults reaching 98%/96% and 96%/96% in children. IBD-REFER outperformed the Red Flags Index when used on the same external validation cohort of IBD cases and controls, with respective ROC values of 0.97 vs 0.78 respectively (P < 0.001).The need for early, accurate identification of patients with inflammatory bowel disease (IBD) has never been more important, especially with rising incidence of IBDThe IBD-REFER criteria are technically precise in the differentiation of known IBD patients from other gastrointestinal (GI) clinic patients without IBD, with relative ease of use. Each major criterion, for which only one element is required for referral, can be obtained from the patient’s stated history. The minor criteria, of which two are required for referral, are a mix of individual and family history, as well some laboratory criteria , which can also be easily obtained or are frequently already available. Notably, additional specialized testing like fecal calprotectin, imaging, or endoscopy are not part of the criteria, adding to the attractiveness of these criteria for ease of use. However, even in the external validation cohort of consecutive patients seen within a GI clinic, the same issues of recall and confirmation bias apply for patients with known diagnoses. Furthermore, the non-IBD controls in this cohort were carefully whittled down from 85 to 50 patients by excluding patients with specific other diagnoses, e.g. celiac disease and lactose intolerance, including 21 (25%) excluded for having additional other diagnoses. This was done with good intention to select an appropriate non-IBD comparison group, but potentially dilutes the true external validity should IBD-REFER be applied in its intended setting; for example, patients presenting to primary care with GI complaints. The performance of the IBD-REFER criteria in a more real-world setting, such as primary care or GI clinic patients without a known diagnosis, is unknown.4 In contrast, despite the availability of multiple GI bleeding scores , these are infrequently used in routine clinical practice. Arguably, this is because such scores predict mortality or re-bleeding, but are less helpful with decision making regarding the overall need for (e.g. to obtain a diagnosis) or relative timing of endoscopy. Such decisions are often the result of reliance on clinical judgement and experience within the local healthcare environment. Viewing the IBD-REFER criteria though this lens, the determination of who is likely to have IBD can be re-interpreted as clinical judgement; particularly the major criteria which are elements of patient history that would normally trigger a high degree of suspicion of IBD. In short, the IBD-REFER criteria seem to represent an attempt to quantify sound clinical judgment; perhaps making it most useful for those who have had less exposure to the diagnosis and care of patients with IBD.Finally, there are arguments and points of view to consider that apply to the use of any clinical tool. First is that many similar tools exist, including for other GI conditions, but few are used in routine clinical practice. This is probably due to a combination of usefulness in real-world situations, ease of use, and reliance on objective criteria. This likely explains the ubiquitous use of the Model for End Stage Liver Disease (MELD), consisting only of three common lab values, with use extending from its initial purpose of predicting survival after transjugular intrahepatic portosystemic shunt (TIPS), to now being central for liver transplant organ allocation, as well as predictive of alcoholic hepatitis survival and preoperative mortality.In summary, the IBD-REFER criteria were rigorously developed with sound methodology, are easy to apply, and have excellent performance characteristics. However, significant limitations to its use include significant reliance on historic recall over objective findings. Additionally, validation was performed on persons with known IBD vs non-IBD diagnoses, and therefore subject to biases that potentially limit external, real-world performance. A prospective investigation of the IBD-REFER criteria in primary care or undiagnosed GI referral patients, with randomization to a comparison group that undergo clinical judgement/usual care, would be of great interest.


Intestinal parasitic infections pose a grave threat to human health, particularly in tropical and subtropical regions. The traditional manual microscopy system of intestinal parasite detection remains the gold standard procedure for diagnosing parasite cysts or eggs. This approach is costly, time-consuming (30 min per sample), highly tedious, and requires a specialist. However, computer vision, based on deep learning, has made great strides in recent years. Despite the significant advances in deep convolutional neural network-based architectures, little research has been conducted to explore these techniques’ potential in parasitology, specifically for intestinal parasites. This research presents a novel proposal for state-of-the-art transfer learning architecture for the detection and classification of intestinal parasite eggs from images. The ultimate goal is to ensure prompt treatment for patients while also alleviating the burden on experts. Our approach comprised two main stages: image pre-processing and augmentation in the first stage, and YOLOv5 algorithms for detection and classification in the second stage, followed by performance comparison based on different parameters. Remarkably, our algorithms achieved a mean average precision of approximately 97% and a detection time of only 8.5 ms per sample for a dataset of 5393 intestinal parasite images. This innovative approach holds tremendous potential to form a solid theoretical basis for real-time detection and classification in routine clinical examinations, addressing the increasing demand and accelerating the diagnostic process. Our research contributes to the development of cutting-edge technologies for the efficient and accurate detection of intestinal parasite eggs, advancing the field of medical imaging and diagnosis. Intestinal parasitic infections have significant implications for public health worldwide, particularly in developing and underdeveloped countries. According to the World Health Organization, infectious and parasitic diseases affect approximately 24% of the global population, and in 2020, preventive chemotherapy was administered to 836 million children globally . These iPreviously, the automation of analyzing intestinal protozoa was limited due to the lack of automatic recognition algorithms for microorganisms under the microscope. However, in recent years, deep learning architectures have revolutionized various machine-learning tasks, particularly in the field of medical image identification and classification. Among these architectures, convolutional neural networks (CNNs) have played a crucial role in significantly improving performance ,7,8. UnlHere, we address the issue of image pre-processing and augmentation for rapid automatic detection and classification in raw parasite images. We have adopted a multi-step routine for data preparation, neural network configuration, model training, and prediction analysis. We applied the You Only Look Once (YOLO) CNN-based model for object detection ,16,17, sWe utilized data augmentation techniques on the source parasite images to generate more regularization and divergence in the training dataset. We attempt to optimize the model by applying an additional dataset and using augmentation and transformation to enhance the results. We were able to accurately detect parasites from a separate test set of microscopic images by using trained YOLOv5 algorithms.Previous efforts in the computational diagnosis of intestinal parasitic infections have primarily focused on improving detection accuracy through hand-engineered feature extraction techniques, which require specialized skills and experts . HoweverIn recent years, the You Only Look Once (YOLO) model for object detection has gained significant attention and has been continuously developed. YOLO directly detects multiple objects by predicting multiple bounding boxes and class probabilities, making it a popular one-stage detection model ,17,28. Whookworm eggs, Hymenolepsis nana, Taenia, Ascaris lumbricoides, and Fasciolopsis buski. These images were obtained from Mulago Referral Hospital, located in Uganda, as well as the IEEE Dataport [https://app.roboflow.com/) (accessed on 17 April 2023). The intestinal parasite dataset used in this research report contains 10× magnification microscopic images with a resolution of 416 × 416 pixels. Five different types of intestinal parasites cysts or eggs collect Dataport ,34. The The images were resized to 416 × 416 for the YOLOv5 algorithms. Finally, the dataset split into training, validation, and testing in the ratios of 70%, 20%, and 10%, respectively.In a recent study, we employed advanced machine learning techniques, specifically YOLOv5 algorithms, to achieve unparalleled accuracy and efficiency in detecting parasites. These state-of-the-art algorithms allowed us to extract the optimal weights from our extensive dataset of 5393 parasite images, encompassing various resolutions. To enhance our results, we conducted experiments with different variants of the YOLOv5 algorithm and trained our models using both YOLOv5s and YOLOv5l object identification models.We selected these particular models based on their ease of implementation on freely available cloud computing platforms, such as the Google Collaboratory, making them accessible to a wider audience. Additionally, the YOLOv5 models offer a diverse range of pre-written model architectures, each tailored to specific speed and accuracy requirements under different conditions. Our models were trained using a dataset of approximately 950 MB, and the training process took only one hour on a Tesla T4 GPU(China). The learned weights and biases of our models were automatically saved into a PyTorch weights file, simplifying the process of reusing and improving our results.As shown in The YOLOv5 object recognition version was cloned from the Ultralytics YOLOv5 catalogue . The YOLIn conclusion, this groundbreaking study highlights the power of advanced machine learning techniques in addressing real-world challenges. By utilizing state-of-the-art algorithms and leveraging cloud computing platforms, we can enhance our ability to detect and combat diseases more effectively. This study is a promising step towards transforming healthcare outcomes globally and advancing the field of machine learning.The experiment was executed on the Google Colab System as the base environment with a Nividia K80/T4 GPU (Taiwan) at 0.82 GHz/1.59 GHz GPU memory clock and GPU memory of 12 GB/16 GB. This experiment was carried out with models built on PyTorch , which pTo determine the strength of the conducted experiments on the trained YOLOv5 algorithms, Precision, Recall, F1-score, and mAP measures are used for evaluation. The computation methods are provided in Equations (1)–(5).TP, TN, FN, and FP represent True Positive (correct detections), True Negative, False Positive (incorrect detections), and False Negative (missed detections), respectively. The F1-score metric, defined in Equation (3), provides a comprehensive evaluation of the trained model by considering the trade-off between Recall and Precision. In addition to the F1-score, the Average Precision (AP) Equation (4) is employed to showcase the overall performance of the models across different thresholds. The following is shown below:r, and In this context, the abbreviations hookworm eggs, Hymenolepis nana, Taenia, Ascaris lumbricoides, and Fasciolopsis. Our collection spans across continents, with images acquired from the Mulago Referral Hospital in Uganda and the IEEE Dataport.In our pursuit to advance parasite detection, we have gathered a diverse dataset of 5883 images featuring various parasites, including With this dataset, we developed a novel detection algorithm using YOLOv5, with each image in the development dataset meticulously annotated with vibrant rectangular boxes as the ground truth. To further improve the robustness of our model, we applied data aumentation and split our dataset into training and testing sets. As we trained our models, we experimented with various image resolutions and settled on 416 × 416 pixels to achieve optimal performance.The YOLOv5 algorithms demonstrated improved detection results for intestinal parasites in the test dataset collection. We computed and compared the precision, recall, F1-score, and AP (average precision) of the detected parasites between the YOLOv5s and YOLOv5l models. The training process utilized the SGD optimizer and lasted for 100 epochs, being completed in 1.04 h for YOLOv5l and 0.918 h for YOLOv5s. Additionally, we employed YOLOv5 features to augment the training images by cropping, adjusting the dynamic range, and changing the scale during the training process. YOLOv5 applied image space and color space augmentations within the training dataset, presenting an original image plus three random images each time an image was loaded for training.Initially, we focused on training the “s” and “l” variants of the YOLOv5 model using the training datasets. We selected the weights with the best available scores as the key indicators of the overall training stage. The scores of the trained neural networks exhibited a significant increase in the 20th and 24th epochs for both models, while the corresponding loss decreased refer to . These eAfter applying pre-trained weights to both YOLOv5 models for validation, we obtained results presented in We evaluated the performance of our proposed methods against four state-of-the-art object detection models: single-shot detector (SSD) , Faster hookworm eggs, Hymenolepis nana, Taenia, Ascaris lumbricoides, and Fasciolopsis. These images were sourced from the Mulago Referral Hospital in Uganda and the IEEE Dataport. The researchers developed a novel detection algorithm using YOLOv5 and meticulously annotated each image in the development dataset with vibrant rectangular boxes as ground truth. The dataset was split into training and testing sets, and data augmentation techniques were applied to enhance the model’s robustness. The YOLOv5 algorithms exhibited favorable detection results, with Precision, Recall, F1-score, and AP metrics calculated and compared between the YOLOv5s and YOLOv5l models. Image and color space augmentations were implemented in the training dataset, presenting an original image plus three random images each time an image was loaded for training.This research paper delves into the utilization of advanced deep learning techniques and cloud computing platforms to enhance parasite detection. The study employed a diverse dataset of 5883 images, encompassing various parasites, such as The study trained both the “s” and “l” variants of the YOLOv5 model using the training datasets, and the best weights were selected based on the overall training stage. The scores of the trained neural networks experienced a significant increase in the 20th and 24th epochs for both models, accompanied by a corresponding decrease in loss, indicating that the neural networks started to identify parasite species. However, as the training process extended to 100 epochs, the validation scores gradually declined after the 90th epoch, and the trained neural networks were unable to effectively recognize parasites.Upon applying pre-trained weights to both YOLOv5 models for validation, the trained neural networks exhibited improved recognition of parasite species, demonstrating slightly higher average mAP (0.5), mAP (0.5:0.95), recall, and precision scores. The YOLOv5l model showcased higher accuracy, but it was slower than the YOLOv5s model due to its increased number of layers and parameters. The researchers provided examples of parasite species detection by YOLOv5l models, showcasing their impressive performance while acknowledging significant distortion in the bounding boxes surrounding the detected species. Overall, the findings of the study suggest that the YOLOv5 models are effective in detecting parasite species and offer valuable insights for future research in this field. The research emphasizes the power of advanced machine learning techniques in addressing real-world challenges and improving disease detection and combat.Hookworm, Taenia, and Fasciolopsis buski parasites was outstanding, achieving a score of 0.99. We attribute this success to the distinct characteristics of hookworms and the abundance of hookworm images in our dataset, which facilitated effective data enhancement during training. The mAP values for Ascaris lumbricoides were also commendable, scoring 0.92 for the YOLOv5 algorithms. However, due to the small dataset size and class imbalance, the training model experienced some degree of overfitting. Despite this limitation, our proposed deep learning model for intestinal parasite detection achieved the highest mAP and demonstrated rapid detection and localization of hookworms, Taenia, Fasciolopsis buski, Ascaris lumbricoides, and Hymenolepis nana, with a mAP of approximately 97% and a detection time of 8.5 ms per image. Future work should aim to expand the dataset size and include other intestinal parasites in the analysis to address the overfitting issue associated with small dataset sizes. Additionally, our study contributes to the development of effective deep learning models that can be utilized for identifying intestinal parasitic cysts or eggs in stool examinations.In this study, our main objective was to enhance the performance of two cutting-edge deep-learning architectures, namely, YOLOv5s and YOLOv5l, in the domain of object detection, specifically focused on intestinal parasites. To achieve this, we conducted a comprehensive evaluation of various metrics, including precision, recall, F1-score, and mean average precision. Our assessment was performed on a dataset consisting of 5393 microscopic images of parasites, each with an input image resolution of 416 × 416. Through systematic analysis of these key parameters, our aim was to optimize the algorithms’ capabilities and improve their accuracy in detecting objects. Our findings revealed that YOLOv5l outperformed YOLOv5s in terms of overall accuracy. However, both algorithms demonstrated excellent performance in recognizing and accurately locating the five types of parasites within the images. Notably, the YOLOv5 algorithms exhibited significantly faster processing times compared to Faser-RCNN  and ResN


The interplay between adipokines and pancreatic beta cells, often referred to as the adipo-insular axis, plays a crucial role in regulating metabolic homeostasis. Adipokines are signaling molecules secreted by adipocytes that have profound effects on several physiological processes. Adipokines such as adiponectin, leptin, resistin, and visfatin influence the function of pancreatic beta cells. The reciprocal communication between adipocytes and beta cells is remarkable. Insulin secreted by beta cells affects adipose tissue metabolism, influencing lipid storage and lipolysis. Conversely, adipokines released from adipocytes can influence beta cell function and survival. Chronic obesity and insulin resistance can lead to the release of excess fatty acids and inflammatory molecules from the adipose tissue, contributing to beta cell dysfunction and apoptosis, which are key factors in developing type 2 diabetes. Understanding the complex interplay of the adipo-insular axis provides insights into the mechanisms underlying metabolic regulation and pathogenesis of metabolic disorders. By elucidating the molecular mediators involved in this interaction, new therapeutic targets and strategies may emerge to reduce the risk and progression of diseases, such as type 2 diabetes and its associated complications. This review summarizes the interactions between adipokines and pancreatic beta cells, and their roles in the pathogenesis of diabetes and metabolic diseases. Adipokines are a group of signaling proteins produced by the adipose tissue. They play key roles in regulating various physiological processes including energy metabolism, inflammation, insulin sensitivity, and appetite control . DisruptThe pancreatic beta cell plays a critical role in maintaining glucose homeostasis. Beta cells synthesize, store, and release insulin, the hormone that is primarily responsible for lowering blood glucose levels. The dysfunction or loss of beta cells, which can arise due to various factors, including autoimmune attacks, inflammation, or metabolic stress, leads to inadequate insulin production and secretion. This results in elevated blood glucose levels, which is a primary characteristic of diabetes. In type 1 diabetes mellitus (T1DM), an autoimmune process targets and destroys beta cells, leading to an absolute insulin deficiency . In contThe interaction between adipose tissue and pancreatic beta cells, known as the adipo-insular axis, plays a pivotal role in metabolic regulation and the onset of metabolic disorders . The priThis review aims to investigate the relationship between adipokines and pancreatic beta cells, evaluate their roles in metabolic homeostasis, and elucidate their implications in the pathogenesis of metabolic disorders.Diabetes mellitus is a chronic metabolic disease characterized by elevated blood glucose levels, accompanied by defects in insulin secretion, insulin action, or both. Diabetes is classified into four categories: T1DM, T2DM, gestational diabetes (GDM), and specific types of diabetes due to other causes . T1DM an+ (KATP) channels in the plasma membrane that close upon ATP binding, increased ATP production promotes the inhibition of outward K+ flux [2+ channels and Ca2+ influx. The elevation of intracellular Ca2+ triggers the fusion of insulin granules with the plasma membrane, leading to increased exocytosis. Several studies have reported that adipokines affect metabolic intermediates, secondary messengers, enzymes, and channels involved in GSIS in beta cells. Consequently, the adipose tissue can communicate with beta cells and modulate the insulin secretion machinery via adipokines, thereby contributing to the maintenance of energy homeostasis.The essential role of pancreatic beta cells is to secrete the appropriate amount of insulin into the bloodstream in response to blood glucose levels. To achieve this goal, the beta cell has a specialized glucose-sensing machinery and a vesicle trafficking system ,14. Gluc K+ flux . This reT2DM is a multifactorial disease affected by both genetic and environmental factors. Generally, the initial event in the development of T2DM is increased peripheral insulin resistance, which is characterized by a decreased response of the body to insulin. However, not everyone with insulin resistance develops T2DM; concurrent beta cell dysfunction is also necessary ,17,18. IIncreased adiposity and ectopic fat storage are major contributors to insulin resistance in peripheral organs such as the liver, adipose tissue, and muscles. Excess nutrients and metabolic stress alter the pathophysiological characteristics of the adipose tissue, leading to changes in adipokine expression and secretion ,22. AlteLeptin plays a crucial role in energy homeostasis and body weight control as well as in regulating glucose homeostasis. This was discovered in a study on an obese (ob) mouse strain, a genetic model of obesity . The disThe leptin receptor, encoded by the LEPR gene, is a member of the class I cytokine receptor family and exists in several isoforms owing to alternative splicing. The long isoform, often referred to as the functional leptin receptor (Ob-Rb), is the primary isoform that mediates a wide range of biological actions . Beta ce2+ influx [Leptin can directly inhibit GSIS in both rodent and human pancreatic beta cells ,35. Howe+ influx . Moreove+ influx . LIM hom+ influx . TherefoThe protective and harmful effects of leptin on beta cells remain controversial. Leptin prevents fatty acid-induced apoptosis by modulating the expression of the anti-apoptotic gene B-cell leukemia 2 (BCL-2) . SimilarFurthermore, the role of leptin in insulin secretion and beta cell proliferation has been investigated in tissue-specific leptin receptor-knockout (KO) mice. Beta cell- and hypothalamus-specific KO mice exhibit impaired GSIS and an increased beta cell mass . SimilarSeveral studies have investigated the effects of hypothalamic leptin and the sympathetic nervous system on beta cell function. One study found that leptin can directly decrease insulin secretion capacity through sympathetic nervous system activation without significantly affecting beta cell mass . FurtherAdiponectin was first reported by four independent research groups between 1995 and 1996, leading to its initial name under various designations, including Acrp30, AdipoQ, apM1, and GBP 28 ,59,60,61Adiponectin has a complex association with insulin secretion. Several studies have demonstrated a significant influence of adiponectin on insulin secretion ,69. GlobSeveral studies have suggested that adiponectin protects beta cells from glucotoxicity-induced apoptosis and dysfunction by activating the AMPK signaling pathway ,76. AdipAdiponectin plays a significant role in the management of insulin resistance and beta cell function, particularly during pregnancy. Alterations in adiponectin levels are associated with the risk and progression of GDM, and may have implications for the future development of T2DM. Weight gain, particularly fat accumulation, is a critical factor in this. Numerous studies have investigated the association between adiponectin levels and beta cell function in GDM. A longitudinal study focusing on Hispanic women with recent GDM revealed that weight gain, particularly fat accumulation, in conjunction with decreased adiponectin and increased C-reactive protein levels was significantly associated with a decline in beta cell function relative to insulin sensitivity . Other sTwo studies investigated the impact of low adiponectin levels, or hypoadiponectinemia, on GDM and beta cell function during pregnancy using mouse models. The first study reported that adiponectin deficiency led to glucose intolerance, hyperlipidemia, and increased fetal body weight in late pregnancy. These metabolic abnormalities were ameliorated by reintroduction of adiponectin, emphasizing its essential role in managing metabolic adaptations during pregnancy . AnotherApelin, originally isolated from bovine stomach extracts, was identified as an endogenous ligand for an orphan GPCR, the apelin receptor (APJ) . Apelin Resistin was initially identified in murine adipose tissue, which sparked significant interest in its potential roles in human metabolism . This inVisfatin, also known as nicotinamide phosphoribosyltransferase (NAMPT) or pre-B cell colony-enhancing factor (PBEF), was initially characterized as an insulin-mimetic adipokine . The oriAdipsin is a serine protease belonging to the trypsin family that plays a pivotal role in the alternative complement pathway ,116,117.Lipocalin-2 (LCN2), also known as neutrophil gelatinase-associated lipocalin (NGAL), is a member of the lipocalin superfamily. Several studies have reported the expression of LCN2 in adipose tissue ,121,122.Chemerin, also known as tazarotene-induced gene 2 (TIG2) or retinoic acid receptor responder 2 (RARRES2), is an adipokine that regulates adipogenesis and energy metabolism and plays a role in the regulation of beta cell mass and function ,126. CheFGF21, a peptide hormone mainly secreted by the liver, regulates glucose and lipid metabolism as well as insulin sensitivity in various organs, including the liver, pancreas, adipose tissue, and muscle ,132,133.GDF15 is a polypeptide that belongs to the TGF-β superfamily and is expressed in various organs, including the liver, adipose tissue, and kidney . In mousTNF-α is an inflammatory cytokine that increases in the adipose tissue of obese individuals and contributes to insulin resistance ,146,147.Following the discovery of the first adipokine, leptin, in 1994 , numerouPancreatic beta cells secrete insulin to regulate blood glucose levels. In T1DM, autoimmunity destroys beta cells, leading to insulin deficiency, whereas in T2DM, insulin resistance and beta cell dysfunction result in insulin deficiency. Adipokines affect the GSIS machinery of beta cells. For example, leptin directly inhibits GSIS, whereas adiponectin activates GSIS via an AMPK-independent pathway.In addition, leptin affects insulin secretion through complex mechanisms involving the modulation of potassium channels, intracellular signaling pathways, and the regulation of apoptosis. Although it inhibits insulin secretion under normal physiological conditions, its effects on beta cell survival and proliferation remain controversial. In contrast, adiponectin increases insulin sensitivity and protects beta cells. It stimulates insulin secretion, prevents apoptosis, and promotes beta cell regeneration, particularly during pregnancy.Apelin, resistin, visfatin, and other adipokines also affect beta cell function and insulin secretion. Apelin influences beta cell homeostasis, and its deficiency can lead to impaired glucose metabolism. The effects of resistin on insulin resistance are not fully understood; however, it appears to influence beta cell apoptosis and insulin receptor expression. Visfatin has been shown to have insulin-mimetic properties and is associated with insulin secretion, beta cell proliferation, and cell viability.Adipsin, lipocalin-2, chemerin, FGF21, GDF15, and TNF-α also contribute to the complex network of interactions between adipokines and beta cells. These adipokines have been shown to influence beta cell mass, insulin secretion, and overall metabolic regulation. Understanding the intricate relationship between adipokines and pancreatic beta cells provides insights into the development of potential treatment strategies for metabolic disorders such as obesity and diabetes. In conclusion, the adipo-insular axis is a critical regulatory system that maintains metabolic homeostasis through the interplay between adipokines and pancreatic beta cells. Dysregulation of this axis may have significant implications in the onset and progression of metabolic disorders, making it a promising area of research for the development of novel therapeutic interventions.Two authors, J.K. and H.K., independently selected relevant studies from PubMed, MeSH, Scopus, Google Scholar, and Embase. The search strategy incorporated the following keywords or subject headings: adipokine, diabetes mellitus, metabolic disorder, insulin-secreting cells, pancreatic beta cell, insulin, leptin, adiponectin, apelin, resistin, visfatin, adipsin, lipocalin-2, chemerin, FGF21, GDF15, and TNF-α. The search was restricted to studies published in English. The authors compared and reviewed the reference lists for potential relevance. The authors discussed the articles, and 152 papers were considered relevant to the search criteria and suitable for addressing the research objective.


Caloric restriction (CR) extends organismal lifespan and health span by improving glucose homeostasis mechanisms. How CR affects organellar structure and function of pancreatic beta cells over the lifetime of the animal remains unknown. Here, we used single nucleus transcriptomics to show that CR increases the expression of genes for beta cell identity, protein processing, and organelle homeostasis. Gene regulatory network analysis link this transcriptional phenotype to transcription factors involved in beta cell identity (Mafa) and homeostasis (Atf6). Imaging metabolomics further demonstrates that CR beta cells are more energetically competent. In fact, high-resolution light and electron microscopy indicates that CR reduces beta cell mitophagy and increases mitochondria mass, increasing mitochondrial ATP generation. Finally, we show that long-term CR delays the onset of beta cell aging and senescence to promote longevity by reducing beta cell turnover. Therefore, CR could be a feasible approach to preserve compromised beta cells during aging and diabetes. Tissues composed of largely post-mitotic cells are expected to be significantly impacted by aging because most cells in these tissues can be remarkably long-lived and as old as the organism itself 4. Because of their longevity, long-lived cells (LLCs) are under an immense pressure to maintain their normal cell function for long time periods to sustain organ function. However, how LLCs achieve and maintain structure and function during adulthood, and whether aging-associated deficits in LLC function in old organisms can be reversed to restore normal tissue function remains largely unknown.Aging is associated with loss of normal cell and tissue function, which are linked to degenerative and metabolic diseases such as Alzheimer’s and diabetes during old age 8. These studies identified that different endocrine cell types in the mouse pancreas are LLCs, including up to 60% of all insulin-secreting beta cells 10. Beta cells secrete insulin in response to increases in blood glucose levels to sustain normal glucose homeostasis for an entire lifetime11. Loss of beta cell insulin secretion can occur during normal aging and is the cause of type 2 diabetes (T2D); this phenotype is linked to compromised expression of transcription factors (TFs) that maintain beta cell identity 14, accumulation of islet fibrosis, inflammation, and ER stress 15, reduced KATP channel conductance16 and loss of coordinated beta cell calcium dynamics 17, impaired beta cell autophagy and accumulation of beta cell DNA damage that triggers beta cell senescence 19. Aging- and T2D-associated impairment of beta cell function are also characterized by an increase in transcriptional noise and re-organization of beta cell gene regulatory networks (GRNs) that normally support beta cell structure-function, including the beta cell ER stress response 20.Using a combination of in vivo stable isotope labelling of mammals (SILAM), correlated and high-resolution electron and multi-isotope mass spectrometry , and protein mass spectrometry pipelines we have identified the tissue distribution of LLCs and of long-lived protein (LLP) complexes in post-mitotic cells 26. These beneficial effects are associated with improved glucose homeostasis due to prolonged fasting and enhanced peripheral insulin sensitivity, enhanced insulin signaling, lower adiposity, enhanced mitochondrial homeostasis and lower ATP generation, increased autophagy, enhanced protein homeostasis and reduced ER stress and inflammation 29. In the pancreas, 20-to-40% CR or CR achieved via TRF are linked to lower islet cell mass in lean mice21, whereas in pre-diabetic mice CR restores normal beta cell secretory function, identity, and preserves beta cell mass33 in a process dependent on activation of beta cell autophagy via Beclin-234. In patients with a recent T2D diagnosis, the efficacy of extreme CR  to reverse T2D depends on the capacity of beta cells to recover from previous exposure to a T2D metabolic state35. However, the mechanisms underlying the adaptation of beta cells to CR and whether CR can delay beta cell aging remains largely unknown.Caloric restriction (CR) and CR-mimicking approaches ) can prolong organismal longevity and delay aging from yeast to non-human primates in vivo and in vitro metabolic phenotyping of beta cell function followed by single cell multiomics and multi-modal high resolution microscopy pipelines  to investigate how CR modulates beta cell heterogeneity and longevity. Our data reveals that CR reduces the demand for beta cell insulin release necessary to sustain euglycemia by increasing peripheral insulin sensitivity. This causes beta cells to undergo a significant reorganization of their transcriptional architecture that promotes a largely post-mitotic and long-lived beta cells with enhanced mitochondrial structure-function while mitigating the onset of aging and senescence signatures. Therefore, our results provide a molecular footprint of how CR promotes beta cell function and longevity to delay aging and that could explain the beneficial effects of CR to the treatment of T2D in humans.We investigated these questions by exposing adult mice to mild CR  for up to 12 months and applied We exposed 8-week-old FVB or mice to 20% CR for 2-months, and quantified in vivo glucose homeostasis mechanisms . As expein vivo, we performed an oral mixed-meal tolerance test (MTT) using a high-carbohydrate nutrient shake . This revealed that CR mice have improved glucose tolerance compared to AL mice, whereas HFD mice were glucose intolerant . These results could be explained by the fact that female mice already have significantly higher peripheral insulin sensitivity than male mice and CR does not further improve insulin sensitivity in relation to AL-fed mice .To quantify the impact of CR on beta cell insulin release and glucose homeostasis tolerant , howevertolerant . Similartolerant –J. The lin vitro. Surprisingly, no significant differences in basal and/or glucose-stimulated insulin release or islet insulin content were observed between diet groups . However, CR beta cells displayed reduced insulin release when challenged with high glucose in combination with the phosphodiesterase inhibitor 3-isobutyl-1-methylxanthine (IBMX) . These results suggested that CR beta cells could have lower glucose-dependent cAMP generation, which is required for amplification of glucose-dependent insulin secretion and incretin signaling37. To test this hypothesis, we repeated the in vivo MTT in AL or CR mice pre-treated with the GLP1 receptor agonist Liraglutide . Pre-treatment with Liraglutide improved glucose tolerance in both groups and sustained higher serum insulin levels after 30 minutes in AL but not in CR mice . Therefore, this data indicates that CR beta cells might have reduced GLP1R signaling in vivo in conditions of meal-induced and transitory hyperglycemia.Next, to measure beta cell insulin release in vitro, we performed dynamic glucose-stimulated insulin secretion assays (GSIS) using isolated islets from male mice exposed to AL, CR, or HFD for 2 months to measure beta cell insulin release Together, these results suggest that CR improves peripheral insulin sensitivity and glucose tolerance in a sex-dependent manner, which decreases the demand for beta cell insulin secretion necessary to maintain normal glucose levels in male mice without significantly affecting glucose-dependent insulin release mechanisms or alpha cell function.in vivo, we applied single nucleus (sn) transposase-accessible chromatin with sequencing (ATAC)- and mRNA sequencing  to investigate the transcriptional architecture underlying the adaptation of beta cells to CR or HFD after 2 months on diet.Given that 2 months of CR was sufficient to trigger significant changes to glucose homeostasis and beta cell insulin secretion Ins1, Ins2), amylin (Iapp), the insulin processing enzyme Pcsk1n, the glucose-6 phosphatase enzyme G6pc2, the beta cell transcription factor (TF) Nkx6–1, and down-regulation of the incretin receptor Gipr (Glp1r was not detected) and of the carbohydrate-responsive transcriptional regulator Mlxipl   and nutrient-dependent regulators of autophagy (Tfeb and Tfe3)  and confocal microscopy of islets from CR mice confirmed that ~80% of all CR beta cells have higher levels of Arntl/Bmal and Pdx1 in situ after 2 months on diet . Pathway enrichment analysis of differentially expressed genes and enriched chromatin regions in CR beta cells revealed the up regulation of pathways linked to beta cell identity and function, oxidative phosphorylation (OxPhos), mitophagy, protein processing, degradation, and transport through the endoplasmic reticulum (ER) and Golgi apparatus , and increased expression of stress- and diabetes-associated genes  , where most changes were associated with genes containing TF motifs mapped to circadian rhythm- (pparatus . In contctively) –I.Ins1, Ins2, Mafa), oxidative and anaerobic carbohydrate metabolism  and mitochondrial structure and function (Cytochrome C genes (Cox4-to-8), Vdac1, Tomm20, and Tomm70a), autophagy , lipid metabolism (Hadh), amino acid transport and metabolism , protein folding and response to ER stress  and DNA damage (Nme1) genes (Gck) and increased expression of glucose-6-phosphatase catalytic subunit 2 (G6pc2), which could suggest that these cells have lower glycolytic flux and increased glucose excursion 40. However, this does not appear to be the case since CR beta cells have appropriate glucose-stimulated insulin release dynamics  and their marker genes and chromatin accessibility profiles ). Beta cTogether, these results indicate that CR triggers significant transcriptional and chromatin accessibility changes in most beta cells that enhance cell identity, nutrient metabolism, circadian rhythm regulation, and ER, mitochondria, and protein homeostasis mechanisms.42. Given that CR has limited effects on beta cell chromatin accessibility  that control beta cell gene expression patterns and heterogeneity Pdx1, Nkx-6.1, Mafa, and Foxa2a (12). This approach revealed the expected and positive correlation in the activation of beta cell TFs   from AL, CR, and HFD mice after 2 months on diet. We identified a total of 242 TF enriched in all beta cells, including beta cell identity TFs d Foxa2, 45. In adRunx1–3) .Mafa, Nkx6–1, Foxo1), protein folding and ER homeostasis , transcriptional regulation of mitochondrial genes (Gabpa/Nrf2 and Gabpb1), NAD metabolism (Sirt6), and glucocorticoid signaling (Nr3c1) genes, which coincides with increased gene expression of some of these markers in CR beta cells  and ER stress response (Atf4) . Similar results were observed for the beta cell proliferation TF Foxp1, and the protein homeostasis TF Crebl2 . Importantly, this beta cell TF target “reprogramming” was reflected in the gene expression pattern of genes associated with Mafa and Atf6. Here, these TFs in CR beta cells were linked to the increased expression of several beta cell identity and protein homeostasis genes , the regulator of intra-golgi transport Arf1, the autophagy marker Lamp1, and beta cell identity and function genes Ucn3, Chga, and Nkx6–1 .Therefore, CR promotes beta cell identity and homeostasis by upregulating the expression of clusters of genes associated with enhanced beta cell maturation, homeostasis, and function. Moreover, this transcriptional program is likely driven by the increased transcriptional activity of in situ islets using imaging mass spectrometry  to quantify the relative abundance and spatial profile of metabolites in islets from AL and CR mice after 2 months on diet. We chose this approach to minimize changes in cell metabolism that could occur during the islet isolation process. Briefly, fresh mouse pancreases were snap-frozen using a liquid nitrogen bath and placed in histology cassettes prior to MALDI-MS metabolite imaging and data analysis (see methods for details)  . Consecudetails) .m/z: 391.22486, CPA 16:0) and nucleotide cytidine 2’ 3’-cyclic phosphate , which is involved in the pyrimidine metabolism pathway. In contrast, islet-enriched metabolites were largely classified as lipids, including cholesterol sulfate (m/z: 465.30443) and phospholipids  .Next, we applied a similar analysis to identify metabolites enriched in AL or CR islets; this identified n=28 ions enriched in AL islets versus n=52 ions enriched in CR islets . Our datTogether, this data shows that CR promotes changes to the overall islet lipid composition and increases the levels of molecules involved in beta cell glucose metabolism and insulin release , and indicates that CR islets could have increased DNA damage response via ADP-ribosylation.12 and DNA damage, both of which are thought to contribute towards beta cell senescence and loss of function during old age 52. We hypothesized CR would prevent the onset of beta cell aging and senescence signatures and to test this hypothesis, we exposed 8-week-old male mice to CR for 12 months  followed by confocal microscopy of mouse pancreases from animals maintained on AL or CR for 2- to 12-months using the DNA damage response marker 53bp1. Here, we observed that CR beta cells have reduced accumulation of 53bp1, thus indicating that these cells accumulate less DNA damage over time (LmnB1 (which is lost in an accelerated model of mouse beta cell senescence 53) and mRNA Fluorescent In Situ Hybridization of p16/Cdkn2a and p21/Cdkn1a genes. We found that CR beta cells have significantly higher LmnB1 levels and reduced in situ expression of p16/Cdkn2a and p21/Cdkn1a  and transcriptional regulators of mitochondrial genes (Gabpa/Gabpb1)  in beta cells from AL or CR mice in situ and found that CR led to significant down-regulation of p-rpS6 in beta cells . This data indicates that mTOR activity is suppressed in beta cells, which would allow beta cells to increase autophagy during CR.Previous data indicates that autophagy is activated by intermitted fasting in HFD-fed mice to maintain beta cell mass and function /Gabpb1) –3. Firstta cells . CR signBeclin1, amp1 and  AL mice –L. Autop55. To determine the role and potential targets of beta cell autophagy during CR, we used deconvolution-assisted confocal colocalization microscopy of mouse pancreases stained with antibodies against lysosomes (Lamp 1), insulin granules (Ins), and/or mitochondria (succinate dehydrogenase isoform A (Sdha)). We found no differences in the co-localization between Ins and Lamp1 across diet groups, which means that CR-induced autophagy does not target mature insulin granules . Importantly, CR beta cells have lower levels of Sdha-Lamp1 colocalization (Prkn) in CR beta cells . To validate these results, we performed high resolution scanning electron microscopy (SEM) of beta cells from AL/CR mice after 2 or 12 months on diet to quantify beta cell mature insulin granule and mitochondrial density in individual cells. As expected, we found no differences in beta cell insulin granule content, and we observed higher mitochondrial mass in CR beta cells – which is likely explained by reduced rates of mitophagy  since its gene expression levels are increased in CR beta cells . p62 targets poly-ubiquitinated (Ub) protein complexes to proteasome and lysosome machineries 56. Confocal microscopy of p62 and Lamp1 revealed that CR increases beta cell p62-vesicle density without affecting the relative co-localization of p62 with lysosomes, thus indicating that these cells undergo a proportional increase in the number of autophagic events and autophagic flux . Moreover, CR beta cells have higher expression of ubiquitin processing and Ub protein degradation genes , including most proteasome subunit genes , thus indicating that CR-induced autophagy in beta cells likely targets poly-ub macromolecules for degradation via the lysosomal and proteasome machineries.Given that CR-induced autophagy in beta cells does not target existing insulin granules or mitochondria, and pathways involved in the degradation of beta cell ER  are not differentially modulated by CR , we decided to measure the protein levels of the adaptor protein p62/Sequestosome-1 ) and higher mitochondrial density  using AL and CR mouse pancreases from our 2-month cohort prepared for SEM ). eTomo provides high-resolution measurements of cellular and mitochondrial structure that can be used to estimate the functional capacity of each mitochondrion to generate ATP and OXPHOS potential from anatomical measurements of mitochondria morphology, cristae number, cristae membrane surface area and density, and type of cristae structure 57. We reconstructed the 3D architecture of individual beta cell mitochondria and the morphology of mitochondrial cristae with an isotropic axial resolution of 1.62nm using 300 nanometer-thick beta cell sections . We estimate that each CR beta cell mitochondria produces ~59,000 ATP molecules/second/mitochondrial volume, which is 14% higher than in AL beta cells . Accordingly, most beta cell MDVs contained membrane-rich cargo that could be seen fusing with lysosomes , while one example fused with the ER. This data suggests that beta cell MDVs are mostly implicated in mitochondrial protein quality control by delivering cargo to the lysosome machinery .While analyzing the eTomo volumes, we noticed the occurrence of vesicle-like structures stemming from the outer membrane regions of beta cell mitochondria that resemble mitochondrial-derived vesicles  microscopy approach  developed previously by us to quantify cell and protein complex age 7. Here, Nitrogen 15 (15N)-labelled animals were created in utero and maintained on 15N-diet until post-natal day 45 (P45). Next, 15N-mice were randomly allocated to AL, CR, or HFD feeding groups using 14N-rich food pellets and maintained on their respective diets for 12 months . Finally, the mice were prepared for MIMS-EM imaging and quantification of cell age based on the amount of 15N retained in the cell nucleus . Importantly, the levels of nuclear 15N in islet and acinar cells were heterogeneous, which is indicative of distinct populations of beta cells with different longevities . We then classified each beta cell as a potential long-lived cell (LLC) or a relatively younger cell according to their nuclear 15N content using cortical neurons as a reference4, and found that up to 80% of all beta cells in CR mice are LLCs  and are implicated as the main causes of T2D 70. CR has been proposed as a feasible approach to combat cardio-metabolic diseases like T2D due to its positive effects on whole body insulin sensitivity that reduce the metabolic demand for insulin release by lowering blood glucose levels. However, here we show that lower insulin release levels occur independently of changes to blood glucose levels. This reduced insulin secretion tone is adequate to maintain euglycemia without triggering hypoglycemic events after meals (even after exposure to incretin receptor agonists). This means that CR promotes a physiological scenario where beta cells “rest” due to a significantly lower metabolic demand for insulin secretion. A similar strategy has been proposed for treatment of T1D and T2D, where exogenous insulin and/or the KATP channel opener diazoxide are applied to recover beta cell function (at least temporarily) 72. To understand how a lower metabolic demand affects the heterogeneity of beta cells during CR, we used single nucleus transcriptomics and found that CR promotes activation of beta cell function, identity, and protein and organelle homeostasis pathways. By profiling beta cell chromatin accessibility and TF activity, we show that CR beta cells activate genes with TF motifs targeted by modulators of circadian , beta cell identity (Mafa), and ER stress and cell homeostasis  pathways. These findings are in line with previous studies showing that CR achieved via time-restricted corrects abnormal glucose homeostasis by activating Dbp33, and that the circadian rhythm regulator Bmal is important for maintenance of mature beta cell identity and function73.Increased insulin sensitivity and/or enhanced insulin signaling during CR are linked to increased longevity in several organisms, from worms to humans Bmal and Pdx1 expression as well as increased density of autophagy vesicles marked by p62/Sqstm1, Lc3I-II, and Lamp1 in CR beta cells in situ. Previous studies have shown that CR and CR via TRF restore beta cell function in HFD-fed animals by rescuing gap-junction connectivity and autophagic fluxes 34. In this context, beta cell autophagy is implicated in the degradation of insulin granules and/or mitochondria to protect beta cells from accumulating molecules and/or damaged organelles during pro-inflammatory and metabolic stress conditions 77. In contrast, our studies reveal that CR-induced autophagy in beta cells does not target insulin granules nor the mitochondria; instead, autophagy is involved in the degradation of poly-ubiquitinated substrates delivered to proteasomes and/or lysosomes via p62-positive autophagy vesicles. Importantly, CR prevented the loss of autophagy vesicles during aging, and reduced beta cell senescence signatures53, as evidenced by a reduction in the accumulation of DNA damage and in the expression of beta cell senescence markers p21/Cdkn1a and p16/Cdkn2a, and in the relative increase in nuclear LmnB1 levels.We validated our transcriptomics using light, electron, and mass spectrometry microscopy; first, we found higher 51, which in turn explains the reduced expression of senescence markers 52. Second, using eTomo microscopy, we discovered that CR increases mitochondrial cristae density that translates into a higher potential of each mitochondrion to generate more ATP. Given that insulin release is reduced in CR cells, this increased ATP production may serve as fuel to sustain ATP-demanding processes activated during CR such as protein ubiquitination and degradation in proteasomes, and autophagy 78. Third, the near absence of MDVs in CR beta cells and up regulation of superoxide scavengers Sod1 and Sod2 strongly suggests ROS generation and degradation of mitochondrial proteins is reduced during CR. Furthermore, previous studies show that CR increases mitochondrial bioenergetic efficiency and reduces oxidative stress accumulation in different systems79 (including in humans80), which is linked to increased mitochondrial protein longevity in aged cells81. Reduced accumulation of oxidative damage (in the form of reactive oxygen species (ROS)) and enhanced bioenergetics of CR beta cells could also explain why these cells secrete less insulin during fasting, as dysregulation of ROS and glycolytic signaling are proposed as causes of basal hyperinsulinemia during diabetes 82. Future experiments will focus on measuring mitochondrial energetics under different basal and stimulated conditions to further understand how CR regulates basal insulin secretion while promoting healthier mitochondria homeostasis in beta cells.The reduced levels of mitophagy observed in CR beta cells are expected to contribute (at least in part) to higher mitochondrial density, while the elevated expression of multiple electron transport chain, cellular respiration, and cristae morphology genes suggest that mitochondrial metabolism and homeostasis are optimized in CR beta cells. This hypothesis is supported by three separate lines of observations. First, our MALDI MS metabolomics experiments reveal normal-to-elevated levels of lipids, cAMP, glucose metabolites, and ADP-ribose – the later which could explain why CR beta cells accumulate less DNA damage15N-SILAM and MIMS-EM we have previously shown that most mouse beta cells are long-lived while the rest constitutes a sub-population of beta cells that proliferates throughout their lifetime 4. Using this same approach, we show that CR prolongs the longevity of beta cells by reducing the turnover rate of the subpopulation of beta cells that is more proliferation prone, while HFD increases beta cell proliferation (as expected). Strikingly, we also found long-lived beta cells in the HFD setting, thus indicating that some beta cells do not proliferate during long periods of hyperglycemia in vivo. Previous studies have shown that proliferation promotes an immature and less functional beta cell phenotype than non-proliferating beta cells 83, which is also supported by the higher levels of beta cell identity and function markers in CR islets. Importantly, this largely post-mitotic state of CR beta cells could also explain the reduced accumulation of DNA damage, as cell proliferation triggers higher accumulation of genetic mutations and replication-dependent stress that can lead to disease and aging 84.Finally, using In conclusion, CR enhances beta cell health and longevity by upregulating cell identity and energy homeostasis mechanisms while delaying beta cell aging. Therefore, these results offer a molecular footprint of how CR affects beta cells to preserve and/or rescue beta cell dysfunction during aging and T2D.65. Therefore, we cannot distinguish between the effects of recurrent and prolonged fasting periods versus reduced daily calorie intake (without fasting) on beta cell function and heterogeneity. Importantly, recent studies show that combining CR with fasting leads to better metabolic and anti-aging effects 65. Our single cell transcriptomics and GRN reconstructions identify likely regulators of the beta cell phenotype described here. Therefore, future experiments will focus on exposing genetic mouse models  to CR. The 15N-SILAM and MIMS-EM experiments add to previous observations of cellular age mosaicism in somatic tissues, including in the pancreas . Like MALDI MS metabolomics and eTomo, this approach is time consuming and financially expensive . Notably, the nuclear 15N measurements of any cell are variable since heterochromatin-rich domains in the sections analyzed are randomly sampled during MIMS-EM. These compartments tend to have higher density of 15N-DNA and yield higher signal intensities versus DNA “poor” euchromatin domains 4. Nevertheless, our results confirm previous MIMS-EM results, and now show how dietary interventions such as CR can be used to promote a long-lived beta cell phenotype.Our report shows how CR impacts mouse beta cell heterogeneity, including their in vivo function and molecular and aging signatures. In our experimental setup, CR was efficiently achieved by providing a restricted amount of food before the start of the animal’s active phase. By design, this approach potentializes the metabolic impacts of CR (including the periods of prolonged fasting) by activating circadian rhythm mechanisms involved in nutrient metabolism