Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of a Machine Learning Algorithm for Structural Brain Images in Chronic Schizophrenia to Earlier Clinical Stages of Psychosis and Autism Spectrum Disorder: A Multiprotocol Imaging Dataset Study

Abstract Background and Hypothesis Machine learning approaches using structural magnetic resonance imaging (MRI) can be informative for disease classification; however, their applicability to earlier clinical stages of psychosis and other disease spectra is unknown. We evaluated whether a model differentiating patients with chronic schizophrenia (ChSZ) from healthy controls (HCs) could be applied to earlier clinical stages such as first-episode psychosis (FEP), ultra-high risk for psychosis (UHR), and autism spectrum disorders (ASDs). Study Design Total 359 T1-weighted MRI scans, including 154 individuals with schizophrenia spectrum (UHR, n = 37; FEP, n = 24; and ChSZ, n = 93), 64 with ASD, and 141 HCs, were obtained using three acquisition protocols. Of these, data regarding ChSZ (n = 75) and HC (n = 101) from two protocols were used to build a classifier (training dataset). The remainder was used to evaluate the classifier (test, independent confirmatory, and independent group datasets). Scanner and protocol effects were diminished using ComBat. Study Results The accuracy of the classifier for the test and independent confirmatory datasets were 75% and 76%, respectively. The bilateral pallidum and inferior frontal gyrus pars triangularis strongly contributed to classifying ChSZ. Schizophrenia spectrum individuals were more likely to be classified as ChSZ compared to ASD (classification rate to ChSZ: UHR, 41%; FEP, 54%; ChSZ, 70%; ASD, 19%; HC, 21%). Conclusion We built a classifier from multiple protocol structural brain images applicable to independent samples from different clinical stages and spectra. The predictive information of the classifier could be useful for applying neuroimaging techniques to clinical differential diagnosis and predicting disease onset earlier.
FOXK2 promotes ovarian cancer stemness by regulating the unfolded protein response pathway

Understanding the regulatory programs enabling cancer stem cells (CSCs) to self-renew and drive tumorigenicity could identify new treatments. Through comparative chromatin-state and gene expression analyses in ovarian CSCs versus non-CSCs, we identified FOXK2 as a highly expressed stemness-specific transcription factor in ovarian cancer. Its genetic depletion diminished stemness features and reduced tumor initiation capacity. Our mechanistic studies highlight that FOXK2 directly regulated IRE1α (encoded by ERN1) expression, a key sensor for the unfolded protein response (UPR). Chromatin immunoprecipitation and sequencing revealed that FOXK2 bound to an intronic regulatory element of ERN1. Blocking FOXK2 from binding to this enhancer by using a catalytically inactive CRISPR/Cas9 (dCas9) diminished IRE1α transcription. At the molecular level, FOXK2-driven upregulation of IRE1α led to alternative XBP1 splicing and activation of stemness pathways, while genetic or pharmacological blockade of this sensor of the UPR inhibited ovarian CSCs. Collectively, these data establish what we believe is a new function for FOXK2 as a key transcriptional regulator of CSCs and a mediator of the UPR, providing insight into potentially targetable new pathways in CSCs.
CRISPR–Cas9 gRNA efficiency prediction: an overview of predictive tools and the role of deep learning

Abstract The clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated protein 9 (Cas9) system has become a successful and promising technology for gene-editing. To facilitate its effective application, various computational tools have been developed. These tools can assist researchers in the guide RNA (gRNA) design process by predicting cleavage efficiency and specificity and excluding undesirable targets. However, while many tools are available, assessment of their application scenarios and performance benchmarks are limited. Moreover, new deep learning tools have been explored lately for gRNA efficiency prediction, but have not been systematically evaluated. Here, we discuss the approaches that pertain to the on-target activity problem, focusing mainly on the features and computational methods they utilize. Furthermore, we evaluate these tools on independent datasets and give some suggestions for their usage. We conclude with some challenges and perspectives about future directions for CRISPR–Cas9 guide design.
Palmitoylation couples the kinases DLK and JNK3 to facilitate prodegenerative axon-to-soma signaling

Dual leucine-zipper kinase (DLK; a MAP3K) mediates neuronal responses to diverse injuries and insults through the c-Jun N-terminal kinase (JNK) family of mitogen-activated protein kinases (MAPKs). Here, we identified two ways through which DLK is coupled to the neural-specific isoform JNK3 to control prodegenerative signaling. JNK3 catalyzed positive feedback phosphorylation of DLK that further activated DLK and locked the DLK-JNK3 module in a highly active state. Neither homologous MAP3Ks nor a homologous MAPK could support this positive feedback loop. Unlike the related JNK1 isoform JNK2 and JNK3 promote prodegenerative axon-to-soma signaling and were endogenously palmitoylated. Moreover, palmitoylation targeted both DLK and JNK3 to the same axonal vesicles, and JNK3 palmitoylation was essential for axonal retrograde signaling in response to optic nerve crush injury in vivo. These findings provide previously unappreciated insights into DLK-JNK signaling relevant to neuropathological conditions and answer long-standing questions regarding the selective prodegenerative roles of JNK2 and JNK3. Description Palmitoylation-dependent colocalization of two kinases mediates retrograde signaling from injured axons. Palmitoylation for axonal degeneration In response to nerve injury, activation of the kinases DLK and JNK3 in axons induces retrograde signaling to the soma, where consequent gene expression changes promote neuronal degeneration. Using cultured neurons and an optic nerve crush model in mice, Niu et al. found that DLK promoted the activation of JNK3 in a localization-dependent manner. Palmitoylation of both kinases mediated their colocalization onto axon vesicles, where a positive feedback phosphorylation loop between the kinases maintained their activation state as the vesicles trafficked to the soma. In addition to revealing a potentially targetable mechanism to reduce neurodegeneration, the findings uncover a role for palmitoylation in axon-to-soma signaling.
A novel and effective technique to reduce electromagnetic radiation absorption on biotic components at 2.45 GHz

ABSTRACT A strong evidence of the effects of radiation absorption on the living community together with a better solution to reduce the radiation intensity without compromising the usage of wireless communication systems is presented. This study analyses the radiation effects on living things and validates the proposed techniques for specific absorption rate (SAR) value reduction at 2.45 GHz. To reduce these radiation impacts on the living community, proper shielding from the radiation and effectively reorienting antenna radiation patterns are the solutions suggested. An analogous antenna configuration in wireless communication systems – a coplanar waveguide fed loop antenna is considered and an open loop resonator (OLR) optimized in ANSYS HFSS at 2.45 GHz is incorporated on the back side of the proposed antenna for achieving SAR value reduction. Theoretical and experimental validation is carried out by measuring the variation in absorption power on each vegetable sample using vector network analyzer E5080A. The existence of OLR on the back side of the antenna reduces the absorption power upto 2 dB. From experimental validation, the proposed technique provides 88% to 98% reduction in SAR value when tested in each sample. Along with this OLR exhibits the capability to enhance the shielding characteristics to the controlled environment of experimental setup for analyzing the stages of seed germination, which helps in reducing the reported radiation effects and growth retardation. The proposed method of EMR reduction with miniaturized planar resonator can be effectively used in the communication systems operating at 2.45 GHz for creating a reduced radiation environment.
Assessment of Artificial Intelligence in Echocardiography Diagnostics in Differentiating Takotsubo Syndrome From Myocardial Infarction.

Importance Machine learning algorithms enable the automatic classification of cardiovascular diseases based on raw cardiac ultrasound imaging data. However, the utility of machine learning in distinguishing between takotsubo syndrome (TTS) and acute myocardial infarction (AMI) has not been studied. Objectives To assess the utility of machine learning systems for automatic discrimination of TTS and AMI. Design, Settings, and Participants This cohort study included clinical data and transthoracic echocardiogram results of patients with AMI from the Zurich Acute Coronary Syndrome Registry and patients with TTS obtained from 7 cardiovascular centers in the International Takotsubo Registry. Data from the validation cohort were obtained from April 2011 to February 2017. Data from the training cohort were obtained from March 2017 to May 2019. Data were analyzed from September 2019 to June 2021. Exposure Transthoracic echocardiograms of 224 patients with TTS and 224 patients with AMI were analyzed. Main Outcomes and Measures Area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity of the machine learning system evaluated on an independent data set and 4 practicing cardiologists for comparison. Echocardiography videos of 228 patients were used in the development and training of a deep learning model. The performance of the automated echocardiogram video analysis method was evaluated on an independent data set consisting of 220 patients. Data were matched according to age, sex, and ST-segment elevation/non-ST-segment elevation (1 patient with AMI for each patient with TTS). Predictions were compared with echocardiographic-based interpretations from 4 practicing cardiologists in terms of sensitivity, specificity, and AUC calculated from confidence scores concerning their binary diagnosis. Results In this cohort study, apical 2-chamber and 4-chamber echocardiographic views of 110 patients with TTS (mean [SD] age, 68.4 [12.1] years; 103 [90.4%] were female) and 110 patients with AMI (mean [SD] age, 69.1 [12.2] years; 103 [90.4%] were female) from an independent data set were evaluated. This approach achieved a mean (SD) AUC of 0.79 (0.01) with an overall accuracy of 74.8 (0.7%). In comparison, cardiologists achieved a mean (SD) AUC of 0.71 (0.03) and accuracy of 64.4 (3.5%) on the same data set. In a subanalysis based on 61 patients with apical TTS and 56 patients with AMI due to occlusion of the left anterior descending coronary artery, the model achieved a mean (SD) AUC score of 0.84 (0.01) and an accuracy of 78.6 (1.6%), outperforming the 4 practicing cardiologists (mean [SD] AUC, 0.72 [0.02]) and accuracy of 66.9 (2.8%). Conclusions and Relevance In this cohort study, a real-time system for fully automated interpretation of echocardiogram videos was established and trained to differentiate TTS from AMI. While this system was more accurate than cardiologists in echocardiography-based disease classification, further studies are warranted for clinical application.
Prostate Cancer Screening, Diagnostic, Treatment Procedures and Costs in Sub-Saharan Africa: A Situational Analysis

Purpose Prostate cancer mortality is predicted to nearly double by 2040 in Sub-Saharan Africa (SSA). The lack of prostate cancer screening in SSA contributes to late-stage diagnosis, treatment delays, and poor survival among patients. We analyzed the availability and use of prostate cancer screening, diagnostic and treatment guidelines, procedures, and costs in few SSA countries to determine factors for consideration in the development of prostate cancer screening guidelines for SSA. Methods We applied mixed methods approaches to collect data through an electronic survey administered to clinicians (oncologists, urologists, pathologists, nurses, and radiation oncologists) providing prostate cancer screening, diagnosis, and treatment services in multiple sub-Saharan countries. Results Inconsistencies in respondents’ understanding of the availability and use of prostate cancer screening guidelines in their countries were noted. Prostate Specific Antigen (PSA) and Digital Rectal Examination (DRE) were the most commonly available screening modalities. Available diagnostic procedures included a combination of prostate biopsies, transrectal ultrasonography, and DRE. Our study’s data suggest that PSA and DRE exams are available for early diagnosis and screening procedures. Availability of treatment modalities with curative intent and costs for prostate cancer related procedures varied between and within countries. Conclusions PSA and DRE are available for detecting prostate cancer and may detect aggressive cancers early, leading to improved outcomes. However, PSA screening is also associated with overdiagnosis and over-treatment. National prostate cancer policies should consider health systems, evidence-based guidelines, population characteristics and healthcare financing to ensure access to clinically relevant and safe prostate cancer related care.
SciKit Digital Health: Python Package for Streamlined Wearable Inertial Sensor Data Processing

Wearable inertial sensors are providing enhanced insight into patient mobility and health. Significant research efforts have focused on wearable algorithm design and deployment in both research and clinical settings; however, open-source, general-purpose software tools for processing various activities of daily living are relatively scarce. Furthermore, few studies include code for replication or off-the-shelf software packages. In this work, we introduce SciKit Digital Health (SKDH), a Python software package (Python Software Foundation) containing various algorithms for deriving clinical features of gait, sit to stand, physical activity, and sleep, wrapped in an easily extensible framework. SKDH combines data ingestion, preprocessing, and data analysis methods geared toward modern data science workflows and streamlines the generation of digital endpoints in “good practice” environments by combining all the necessary data processing steps in a single pipeline. Our package simplifies the construction of new data processing pipelines and promotes reproducibility by following a convention over configuration approach, standardizing most settings on physiologically reasonable defaults in healthy adult populations or those with mild impairment. SKDH is open source, as well as free to use and extend under a permissive Massachusetts Institute of Technology license, and is available from GitHub (PfizerRD/scikit-digital-health), the Python Package Index, and the conda-forge channel of Anaconda.
The Deformation of the Wire Cable about the Hoist with Double Cages and Cage Amplitude Analysis

Based on the elastic deformation's formula of the wire cable about the hoist with single cage,and considered the influences of various elements,just as the air resistance caused by cage's movement,of each bamboo cable weir about the hoist with double cages,the elastic deformation's formula was deduced.By the formula and software MATLAB-SIMULINK,the vibration model of raised cage was built,after inputted the raw data,we get the vibration results under simulation.
Cortico-Striatal Control over Adaptive Goal-Directed Responding Elicited by Cues Signaling Sucrose Reward or Punishment

The medial prefrontal cortex (mPFC) and nucleus accumbens (NAc) have been associated with the expression of adaptive and maladaptive behavior elicited by fear-related and drug-associated cues. However, reported effects of mPFC manipulations on cue-elicited natural reward-seeking and inhibition thereof have been varied, with few studies examining cortico-striatal contributions in tasks that require adaptive responding to cues signaling reward and punishment within the same session. The current study aimed to better elucidate the role of mPFC and NAc subdivisions, and their functional connectivity in cue-elicited adaptive responding using a novel discriminative cue responding task. Male Long–Evans rats learned to lever-press on a VR5 schedule for a discriminative cue signaling reward, and to avoid pressing the same lever in the presence of another cue signaling punishment. Postacquisition, prelimbic (PL) and infralimbic (IL) areas of the mPFC, NAc core, shell, PL-core, or IL-shell circuits were pharmacologically or chemogenetically inhibited while animals performed under (1) nonreinforced (extinction) conditions, where the appetitive and aversive cues were presented in alternating trials alone or as a compound stimulus; and (2) reinforced conditions, whereby cued responding was accompanied by associated outcomes. PL and IL inactivation attenuated nonreinforced and reinforced goal-directed cue responding, whereas NAc core and shell inactivation impaired nonreinforced responding for the appetitive, but not aversive cue. Furthermore, PL-core and IL-shell inhibition disinhibited nonreinforced but not reinforced cue responding. Our findings implicate the mPFC as a site of confluence of motivationally significant cues and outcomes, and in the regulation of nonreinforced cue responding via downstream NAc targets. SIGNIFICANCE STATEMENT The ability to discriminate and respond appropriately to environmental cues that signal availability of reward or punishment is essential for survival. The medial prefrontal cortex (mPFC) and nucleus accumbens (NAc) have been implicated in adaptive and maladaptive behavior elicited by fear-related and drug-associated cues. However, less is known about the role they play in orchestrating adaptive responses to natural reward and punishment cues within the same behavioral task. Here, using a novel discriminative cue responding task combined with pharmacological or chemogenetic inhibition of mPFC, NAc and mPFC-NAc circuits, we report that mPFC is critically involved in responding to changing cued response-outcomes, both when the responses are reinforced, and nonreinforced. Furthermore, the mPFC coordinates nonreinforced discriminative cue responding by suppressing inappropriate responding via downstream NAc targets.
What are the factors predictive of postoperative complications in patients with colorectal cancer undergoing stenting as a bridge to surgery?

To the Editors, We have read with interest the article published by Adachi et al. This study is of great significance and plays an important guiding role in our clinical work. A few months ago, a patient with obstruction caused by colon cancer was admitted to our hospital. We placed a metal stent under endoscopy, creating an opportunity for subsequent surgery (Figure 1). Although we believe it is a very interesting topic, we would like to offer the following points for your consideration. In Table 1, I think the following factors need to be included in the study because they may be related to postoperative complications. First, in Table 1, I think the surgical methods of the two groups (absent group and present group) need to be included in the study, such as minimally invasive surgery and open surgery. Many studies have shown that minimally invasive surgery has the advantages of less trauma and lower incidence of postoperative complications than open surgery. Second, in Table 1, I think the preoperative diseases of two groups need to be included in the study, such as lung disease, cardiovascular disease, and diabetes. Patients with chronic obstructive pulmonary disease are prone to respiratory depression during and after the operation, which can increase the risk of postoperative pulmonary infection. Patients with cardiovascular disease may have an increased risk of postoperative bleeding and incision infection due to long‐term use of anticoagulants. Patients with diabetes can affect the normal nutritional metabolism of the body and reduce the immune function, it can also lead to delayed healing of the incision and increase the risk of wound infection.
Kiloparsec-scale Imaging of the CO(1-0)-traced Cold Molecular Gas Reservoir in a z ∼ 3.4 Submillimeter Galaxy

We present a high-resolution study of the cold molecular gas as traced by CO(1-0) in the unlensed z ∼ 3.4 submillimeter galaxy SMM J13120+4242, using multiconfiguration observations with the Karl G. Jansky Very Large Array (JVLA). The gas reservoir, imaged on 0. ″39 (∼3 kpc) scales, is resolved into two components separated by ∼11 kpc with a total extent of 16 ± 3 kpc. Despite the large spatial extent of the reservoir, the observations show a CO(1-0) FWHM linewidth of only 267 ± 64 km s−1. We derive a revised line luminosity of LCO(1−0)′ = (10 ± 3) × 1010 K km s−1 pc2 and a molecular gas mass of M gas = (13 ± 3)× 1010 (α CO/1) M ⊙. Despite the presence of a velocity gradient (consistent with previous resolved CO(6-5) imaging), the CO(1-0) imaging shows evidence for significant turbulent motions that are preventing the gas from fully settling into a disk. The system likely represents a merger in an advanced stage. Although the dynamical mass is highly uncertain, we use it to place an upper limit on the CO-to-H2 mass conversion factor α CO of 1.4. We revisit the SED fitting, finding that this galaxy lies on the very massive end of the main sequence at z = 3.4. Based on the low gas fraction, short gas depletion time, and evidence for a central AGN, we propose that SMM J13120 is in a rapid transitional phase between a merger-driven starburst and an unobscured quasar. The case of SMM J13120 highlights how mergers may drive important physical changes in galaxies without pushing them off the main sequence.