bluuebunny/crossref_metadata_2025 huggingface dataset:
Predictors and Variation in Steroid Injection Use for Carpal Tunnel Syndrome from a Multicenter Quality Collaborative
Authors: Billig Jessica I. , Kotsis Sandra V. , Kong Lingxuan , Wang Lu , Chung Kevin C. ,   Michigan Collaborative Hand Initiative for Quality in Surgery (M-CHIQS) Group*
Published: June 2023

Background:

Steroid injections are commonly used as first-line treatment for carpal tunnel syndrome (CTS); however, research has shown that their benefit is generally short-term and many patients go on to receive carpal tunnel release. The study purpose was to determine the variation in steroid injection use by hand surgeons.

Methods:

We analyzed data from a 9-center hand surgery quality collaborative. Data from 1,586 patients (2,381 hands) were included if they underwent elective CTR at one of the sites. Mixed effects logistic regression models were used to examine the association of receipt of steroid injection and association of receipt of more than one steroid injection among patient-level covariates.

Results:

Steroid injection use significantly varied by practice, ranging from 12-53% of patients. The odds of receiving a steroid injection were 1.4 times higher for females (p<0.01), 1.6 times higher for patients with chronic pain syndrome (p<0.01), 0.5 times lower for patients with moderate electromyography (EMG) and 0.4 times lower for patients with severe EMG classification (both p<0.01). Patients with high CTS-6 scores (p=0.02) and patients with moderate (p=0.04) or severe EMG (p=0.05) had lower odds of receiving multiple steroid injections. Complete symptomatic improvement after steroid injection was significantly reported by patients with high CTS-6 score (p=0.03) or patients with severe EMG classification (p=0.02).

Conclusions:

We found wide patient-level and practice-level variation in the use of steroid injections prior to undergoing CTR. These findings underscore the need for improved data and standard practice guidelines regarding which patients benefit from steroid injection.
Oligodendrocyte Transcription Factor 2 as a Potential Prognostic Biomarker of Glioblastoma: Kaplan-Meier Analysis and the Development of a Binary Predictive Model Based on Visually Accessible Rembrandt Image and Magnetic Resonance Imaging Radiomic Features
Authors: Mei Nan , Lu Yiping , Yang Shan , Jiang Shenghong , Ruan Zhuoying , Wang Dongdong , Liu Xiujuan , Ying Yinwei , Li Xuanxuan , Yin Bo
Published: June 2023

Objective

Oligodendrocyte transcription factor 2 (OLIG2) is universally expressed in human glioblastoma (GB). Our study explores whether OLIG2 expression impacts GB patients' overall survival and establishes a machine learning model for OLIG2 level prediction in patients with GB based on clinical, semantic, and magnetic resonance imaging radiomic features.

Methods

Kaplan-Meier analysis was used to determine the optimal cutoff value of the OLIG2 in 168 GB patients. Three hundred thirteen patients enrolled in the OLIG2 prediction model were randomly divided into training and testing sets in a ratio of 7:3. The radiomic, semantic, and clinical features were collected for each patient. Recursive feature elimination (RFE) was used for feature selection. The random forest (RF) model was built and fine-tuned, and the area under the curve was calculated to evaluate the performance. Finally, a new testing set excluding IDH-mutant patients was built and tested in a predictive model using the fifth edition of the central nervous system tumor classification criteria.

Results

One hundred nineteen patients were included in the survival analysis. Oligodendrocyte transcription factor 2 was positively associated with GB survival, with an optimal cutoff of 10% (P = 0.00093). One hundred thirty-four patients were eligible for the OLIG2 prediction model. An RFE-RF model based on 2 semantic and 21 radiomic signatures achieved areas under the curve of 0.854 in the training set, 0.819 in the testing set, and 0.825 in the new testing set.

Conclusions

Glioblastoma patients with ≤10% OLIG2 expression tended to have worse overall survival. An RFE-RF model integrating 23 features can predict the OLIG2 level of GB patients preoperatively, irrespective of the central nervous system classification criteria, further guiding individualized treatment.
Diagnostic Value of Computed Tomography Angiography in Suspected Acute Ischemic Stroke Patients With Respect to National Institutes of Health Stroke Scale Score
Authors: Andrée Daniel , Thanabalasingam Arjun , Teubner Jonas , Fahrni Markus , Potthast Silke
Published: June 2023

Objective

Noncontrast computed tomography (NCCT) plus computed tomography angiography (CTA) is the standard imaging modality for acute stroke. We investigated whether there is an additional diagnostic value of supra-aortic CTA in relation to National Institutes of Health Stroke Scale (NIHSS) and resultant effective radiation dose.

Methods

In this observational study, 788 patients with suspected acute stroke were included and divided into 3 NIHSS groups: group 1, NIHSS 0–2; group 2, NIHSS 3–5; and group 3, NIHSS ≥ 6.

Computed tomography scans were assessed for findings of acute ischemic stroke and vascular pathologies in 3 regions. Final diagnosis was obtained from medical records. Effective radiation dose was calculated based on the dose-length product.

Results

Seven hundred forty-one patients were included. Group 1 had 484 patients, group 2 had 127 patients, and group 3 had 130 patients. Computed tomography diagnosis of acute ischemic stroke was made in 76 patients. In 37 patients, a diagnosis of acute stroke was made based on pathologic CTA findings in case of an unremarkable NCCT. Stroke occurrence was the lowest in groups 1 and 2, with 3.6% and 6.3%, respectively, compared with 12.7% in group 3. If both NCCT and CTA were positive, the patient was discharged with a stroke diagnosis. Male sex had the highest effect on the final stroke diagnosis. The mean effective radiation dose was 2.6 mSv.

Conclusions

In female patients with NIHSS 0–2, additional CTA rarely contains relevant additional findings decisive for treatment decisions or overall patient outcomes; therefore, CTA in this patient group might yield less impactful findings, and the applied radiation dose could be lowered by approximately 35%.
Whole-Lesion Apparent Diffusion Coefficient Histogram Analysis for Assessing Normal-Sized Lymph Node Metastasis in Cervical Cancer: Comparison Between Readout-Segmented and Single-Shot Echo-Planar Diffusion-Weighted Imaging
Authors: Qian Weiliang , Chen Qian , Hu Chunhong
Published: June 2023

Objective

To compare the value of whole-lesion apparent diffusion coefficient (ADC) histogram analysis derived from readout-segmented echo-planar imaging (RS-EPI) and single-shot echo-planar imaging (SS-EPI) diffusion-weighted imaging (DWI) in evaluating normal-sized lymph node metastasis (LNM) in cervical cancer.

Methods

Seventy-six pathologically confirmed cervical cancer patients (stages IB and IIA) were enrolled, including 61 patients with non-LNM (group A) and 15 patients with normal-sized LNM (group B). The recorded tumor volume on T2-weighted imaging was the reference against which both DWIs were evaluated. Each ADC histogram parameter (including ADCmax, ADC90, ADCmedian, ADCmean, ADC10, ADCmin, ADCskewness, ADCkurtosis, and ADCentropy) was compared between SS-EPI and RS-EPI and between the 2 groups.

Results

There was no significant difference in tumor volume between the 2 DWIs and T2-weighted imaging (both P > 0.05). Higher ADCmax and ADCentropy but lower ADC10, ADCmin and ADCskewness were found in SS-EPI than those in RS-EPI (all P < 0.05). For SS-EPI, lower ADC90 and higher ADCkurtosis were found in group B than those in group A (both P < 0.05). For RS-EPI, lower ADC90 and higher ADCkurtosis and ADCentropy were found in group B than those in group A (all P < 0.05). Readout-segmented echo-planar imaging ADCkurtosis showed the highest area under the curve of 0.792 in the differentiation of the 2 groups (sensitivity, 80%; specificity, 73.77%).

Conclusions

Compared with SS-EPI, the ADC histogram parameters derived from RS-EPI were more accurate, and ADCkurtosis held great potential in differentiating normal-sized LNM in cervical cancer.
Radiomics of Spinal Metastases Originating From Primary Nonsmall Cell Lung Cancer or Breast Cancer and Ability to Predict Epidermal Growth Factor Receptor Mutation/Ki-67 Levels
Authors: Niu Shuxian , Zhang Hongxiao , Wang Xiaoyu , Jiang Wenyan
Published: June 2023

Objectives

The aims of the study are to explore spinal magnetic resonance imaging (MRI)-based radiomics to differentiate spinal metastases from primary nonsmall cell lung cancer (NSCLC) or breast cancer (BC) and to further predict the epidermal growth factor receptor (EGFR) mutation and Ki-67 expression level.

Methods

In total, 268 patients with spinal metastases from primary NSCLC (n = 148) and BC (n = 120) were enrolled between January 2016 and December 2021. All patients underwent spinal contrast-enhanced T1-weighted MRI before treatment. Two- and 3-dimensional radiomics features were extracted from the spinal MRI images of each patient. The least absolute shrinkage and selection operator regression were applied to identify the most important features related to the origin of the metastasis and the EGFR mutation and Ki-67 level. Radiomics signatures (RSs) were established using the selected features and evaluated using receiver operating characteristic curve analysis.

Results

We identified 6, 5, and 4 features from spinal MRI to develop Ori-RS, EGFR-RS, and Ki-67-RS for predicting the metastatic origin, EGFR mutation, and Ki-67 level, respectively. The 3 RSs performed well in the training (area under the receiver operating characteristic curves: Ori-RS vs EGFR-RS vs Ki-67-RS, 0.890 vs 0.793 vs 0.798) and validation (area under the receiver operating characteristic curves: Ori-RS vs EGFR-RS vs Ki-67-RS, 0.881 vs 0.744 vs 0.738) cohorts.

Conclusions

Our study demonstrated the value of spinal MRI-based radiomics for identifying the metastatic origin and evaluating the EGFR mutation status and Ki-67 level in patients with NSCLC and BC, respectively, which may have the potential to guide subsequent individual treatment planning.
Can Machine Learning Identify the Intravenous Contrast Dose and Injection Rate Needed for Optimal Enhancement on Dynamic Liver Computed Tomography?
Authors: Masuda Takanori , Nakaura Takeshi , Funama Yoshinori , Sato Tomoyasu , Nagayama Yasunori , Kidoh Masafumi , Yoshida Masato , Arao Shinichi , Ono Atsushi , Hiratsuka Junichi , Hirai Toshinori , Awai Kazuo
Published: June 2023

Objectives

This study aimed to investigate whether machine learning (ML) is useful for predicting the contrast material (CM) dose required to obtain a clinically optimal contrast enhancement in hepatic dynamic computed tomography (CT).

Methods

We trained and evaluated ensemble ML regressors to predict the CM doses needed for optimal enhancement in hepatic dynamic CT using 236 patients for a training data set and 94 patients for a test data set. After the ML training, we randomly divided using the ML-based (n = 100) and the body weight (BW)–based protocols (n = 100) by the prospective trial. The BW protocol was performed using routine protocol (600 mg/kg of iodine) by the prospective trial. The CT numbers of the abdominal aorta and hepatic parenchyma, CM dose, and injection rate were compared between each protocol using the paired t test. Equivalence tests were performed with equivalent margins of 100 and 20 Hounsfield units for the aorta and liver, respectively.

Results

The CM dose and injection rate for the ML and BW protocols were 112.3 mL and 3.7 mL/s, and 118.0 mL and 3.9 mL/s (P < 0.05). There were no significant differences in the CT numbers of the abdominal aorta and hepatic parenchyma between the 2 protocols (P = 0.20 and 0.45). The 95% confidence interval for the difference in the CT number of the abdominal aorta and hepatic parenchyma between 2 protocols was within the range of predetermined equivalence margins.

Conclusions

Machine learning is useful for predicting the CM dose and injection rate required to obtain the optimal clinical contrast enhancement for hepatic dynamic CT without reducing the CT number of the abdominal aorta and hepatic parenchyma.
Community-based end-of-life care in Singapore and nursing care implications for older adults in the post-COVID-19 world
Authors: Poon Edward , Ang Seng Hock Martin , Ramazanu Sheena
Published: June 2023

Purpose of review

To render holistic overview on community-based end-of-life care in the context of Singapore, with analysis of nursing care implications for older adults requiring end-of-life care services.

Recent findings

Healthcare professionals caring for older adults with life-limiting conditions had to play an active role in the constantly evolving healthcare landscape during the coronavirus disease 2019 (COVID-19) pandemic. Usual meetings and community-based end-of-life care interventions were converted to online mode, utilizing digital technology. In order to provide value-based and culturally relevant care, further studies are warranted to evaluate healthcare professionals, patients and family caregivers’ preferences whilst utilizing digital technology. As a result of COVID-19 pandemic restrictions to minimize infection transmissions, animal-assisted volunteering activities were conducted virtually. Regular healthcare professionals’ engagement in wellness interventions is necessary to boost morale and prevent potential psychological distress.

To strengthen the delivery of end-of-life community care services, the following recommendations are proposed: active youth engagement via inter-collaborations and connectedness of community organizations; improving support for vulnerable older adults in need of end-of-life care services; and enhancing healthcare professionals well-being through the implementation of timely support interventions.
Navigating the pharmacologic complexities of QTc prolongation: assessing the cumulative burden in individuals with serious mental illness
Authors: Demler Tammie Lee , O’Donnell Carolyn
Published: June 2023

The scientific progress made in the pharmacologic management of serious mental illness (SMI) has been significant. However, the benefits of medication management must continually be weighed against the risks of adverse effects of the agents prescribed. While many medications pose increased danger of QTc prolongation, which may result in malignant arrhythmia and sudden cardiac death, the combination of medications contributing QTc risk can result in an impact of unpredictable pharmacodynamic magnitude. Pharmacists play a key role in communicating QTc risks to prescribers, however little if any clinical guidance informs clinicians of specific actions to take when initiating or continuing a clinically necessary combination that poses risk. This study is a cross-sectional view of Med Safety Scan (MSS) QT prolongation risk scores, generated from the ranking tool available from the CredibleMeds website, intended to provide greater understanding of overall risk of QT burden to assist in the prescribing of medications to patients with SMI in a psychiatric hospital.
Vibrio syngnathi sp. nov., a fish pathogen, isolated from the Kiel Fjord
Authors: Chibani Cynthia Maria , Hertel Robert , Neumann-Schaal Meina , Goehlich Henry , Wagner Kim , Bunk Boyke , Spröer Cathrin , Overmann Jörg , Hoppert Michael , Marten Silke Mareike , Roth Olivia , Liesegang Heiko , Wendling Carolin C.
Published: June 2023

A new

Vibrio

strain, K08M4T, was isolated from the broad-nosed pipefish Syngnathus typhle in the Kiel Fjord. Infection experiments revealed that K08M4T was highly virulent for juvenile pipefish. Cells of strain K08M4T were Gram-stain-negative, curved rod-shaped and motile by means of a single polar flagellum. The strain grew aerobically at 9–40° C, at pH 4–10.5 and it tolerated up to 12 % (w/v) NaCl. The most prevalent (>10 %) cellular fatty acids of K08M4T were C16 : 1
ω7c and C16 : 0. Whole-genome comparisons revealed that K08M4T represents a separate evolutionary lineage that is distinct from other

Vibrio

species and falls within the Splendidus clade. The genome is 4,886,292 bp in size, consists of two circular chromosomes (3,298,328 and 1, 587,964 bp) and comprises 4,178 protein-coding genes and 175 RNA genes. In this study, we describe the phenotypic features of the new isolate and present the annotation and analysis of its complete genome sequence. Based on these data, the new isolate represents a new species for which we propose the name Vibrio syngnathi sp. nov. The type strain is K08M4T (=DSM 109818T=CECT 30086T).
Household transmission of non-toxigenic diphtheria toxin gene-bearing Corynebacterium diphtheriae following a cluster of cutaneous cases in a specialist outpatient setting
Authors: Fry Norman K. , Pringle Ellen , Newsholme William , Nicholls Margot , Stephenson Jim , Heathcock Rachel Thorn , Gower Charlotte , Lacy Joanne , O’Boyle Shennae , Litt David J. , Sheppard Carmen , Groves Natalie , D’Aeth Joshua , Hopkins Katie L. , Meunier Danièle , De Zoysa Aruni , Efstratiou Androulla , Brown Colin , Chand Meera , Amirthalingam Gayatri
Published: June 2023

Introduction. Combination of PCR and Elek testing to identify toxigenic corynebacteria has revealed organisms described as non-toxigenic toxin-gene bearing (NTTB)

Corynebacterium diphtheriae

or

C. ulcerans

(i.e. PCR tox positive; Elek negative). These organisms carry part or all of tox, but are unable to express diphtheria toxin (DT) and present a challenge to clinical and public health case management.

Gap analysis/Hypothesis. There are few data on the theoretical risk of NTTB reversion to toxigenicity. This unique cluster and subsequent epidemiologically linked isolates allowed the opportunity to determine any change in DT expression status.

Aim. To characterize a cluster of infections due to NTTB in a skin clinic and subsequent cases in two household contacts.

Methodology. Epidemiological and microbiological investigations were carried out according to existing national guidance at the time. Susceptibility testing used gradient strips. The tox operon analysis and multi-locus sequence typing (MLST) was derived from whole-genome sequencing. Alignment of the tox operon and phylogenetic analyses were performed using clustalW, mega, the public core-genome MLST (cgMLST) scheme and an in-house bioinformatic single nucleotide polymorphism (SNP) typing pipeline.

Results. Isolates of NTTB

C. diphtheriae

were recovered from four cases (cases 1 to 4) with epidermolysis bullosa attending the clinic. Two further isolates were subsequently recovered from case 4, >18 months later, and from two household contacts (cases 5 and 6) after a further 18 months and 3.5 years, respectively. All eight strains were NTTB

C. diphtheriae

biovar mitis, belonged to the same sequence type (ST-336) with the same deletion in tox. Phylogenetic analysis showed relatively high diversity between the eight strains with 7–199 SNP and 3–109 cgMLST loci differences between them. The number of SNPs between the three isolates from case 4 and two household contacts (cases 5 and 6) was 44–70 with 28–38 cgMLST loci differences.

Conclusions. We report a cluster of NTTB

C. diphtheriae

cases in a skin clinic and evidence of onward household transmission. We conclude the deletion in the tox was responsible for the non-expression of DT. There was no evidence of reversion to DT expression over the 6.5 year period studied. These data informed revision to guidance in the management of NTTB cases and their contacts in the UK.
Demography, hygiene and previous disease prevalence as plausible risk factors associated with Covid-19 deaths across Indian states
Authors: Chatterjee Bithika , Mande Shekhar C.
Published: June 2023

Severity of Covid-19 diseases has been disproportionate with higher case-fatality ratio affecting developed nations. In India, states with higher income have reported more number of deaths compared to lower income states. The global burden of diseases India 2019 and the National Health Profile 2019 data was used to draw correlations with Covid-19 mortality at two different dates of peak Covid-19 cases in India. We explored correlation of mortality in different states of India with prevalence of different diseases, demography, development, sanitation etc. The study found a positive correlation with known demographic parameters such as percentage of elderly population(spearman correlation coefficient(rho) =0.44 and 0.46 with 1stand 2ndpeak respectively). Similarly, percentage urbanization was seen to correlate well with mortality(rho=0.71 and 0.57) suggesting Covid-19 to be a predominantly urban disease. Prevalence of Autoimmune diseases, and Cancer show higher correlation with deaths. A surprising positive correlation emerged between improved sanitation parameters, such as closed drainage and indoor toilets, with COVID-19 deaths. Overall the multivariate regression model achieved by combining demography, sanitation, autoimmune diseases and cancer gave us the best prediction for Covid-19 mortality(adjusted R square value of 0.71 with peak 1 and 0.85 with peak 2). Analysis of the Covid-19 related data seems to indicate that as the wealth of a state increases, the state’s urban landscape changes often leading to better sanitation facilities. The lifestyle and prevalence to autoimmune diseases as well as cancer also increases. However, this may affect the state’s ability to fight pandemics due to lower exposure to pathogens and immune training.
Dual-energy computed tomography imaging with megavoltage and kilovoltage x-ray spectra
Authors: Jadick Giavanna , Schlafly Geneva , La Rivière Patrick J.
Published: June 2023

Purpose

Single-energy computed tomography (CT) often suffers from poor contrast, yet it remains critical for effec-tive radiotherapy treatment. Modern therapy systems are often equipped with both megavoltage (MV) and kilovoltage (kV) x-ray sources and thus already possess the hardware needed for dual-energy (DE) CT. There exists an unexplored potential for enhanced image contrast using MV-kV DE-CT in radiotherapy contexts.

Approach

A toy model comprising a single-line integral through a two-material object was designed for computing basis material signal-to-noise ratio (SNR) using estimation theory. Five dose-matched spectra (three kV, two MV) and three variables were considered: spectral combination, spectral dose allocation, and object material composition. The single-line model was extended to a simulated fan-beam CT acquisition of an anthropomorphic phantom with and without a metal implant. Basis material sinograms were computed and synthesized into virtual monoenergetic images (VMIs). MV-kV and kV-kV VMIs were compared with single-energy images.

Results

The 80kV-140kV pair typically yielded the best SNRs, but for bone thicknesses greater than 8 cm, the detunedMV-80kV pair surpassed it. Peak MV-kV SNR was achieved with approximately 90% dose allocated to the MV spectrum. For the CT simulations, MV-kV VMIs yielded a higher contrast-to-noise ratio (CNR) than single-energy CT at specific monoenergies. With the metal implant, MV-kV produced a higher maximum CNR and lower minimum root-mean-square-error than kV-kV.

Conclusions

This work quantitatively analyzes MV-kV DE-CT imaging and assesses its potential advantages. This technique may yield improved contrast and accuracy relative to dose-matched single-energy CT or kV-kV DE-CT, depending on object composition.
SOCIAL MEDIA AS AN INFORMATION, EDUCATION, AND COMMUNICATION TOOL FOR RABIES PREVENTION: AN INTERVENTIONAL STUDY
Authors: Verma Jay , Agarwal Anurag
Published: June 2023

Introduction

Rabies is a fatal disease that can be avoided by treating animal bites promptly. Hence, post-exposure prophylaxis is critical. As a result, the National Rabies Control Program was approved under the 12th five-year plan. One of its strategies is to engage in Information, Education and Communication activities. Social media provides an opportunity for the quick and easy dissemination of research but is constrained by a lack of peer review and the risk of misinterpretation. The efficacy of a novel social media-based knowledge dissemination strategy for rabies prevention was tested in this study.

Methods

An experimental study design was followed, wherein 144 preclinical medical students were included in each control and test group. The test group was administered the intervention, which exposed the participants to health education material via social media across a span of 30 days. Participants’ knowledge, attitude, and practices were observed before and after the study duration.

Results

There was no significant change in the responses of the control (chi-square statistic = 2.46, p-value = 0.96) and test (chi-square statistic = 1.64, p-value = 0.98) groups across the study duration. A comparison of the change in responses between the two groups revealed an insignificant difference (chi-square statistic = 1.29, p-value = 0.73).

Conclusion

Social media is an ineffective strategy for improvements in health literacy over a short duration. Such strategies need further validation for longer durations. Moreover, the effectiveness of the health education material itself needs to be assessed.
The pattern of antidiabetic drugs and glycaemic control among type 2 diabetes patients in an Endocrinology Clinic in Lagos, Nigeria
Authors: Azeez Taoreed Adegoke
Published: June 2023

Background

Diabetes mellitus is highly prevalent in Nigeria. In addition to lifestyle changes, hypoglycaemic agents are of crucial importance in providing optimal care. The study aimed to study the pattern of hypoglycaemic agents and glycaemic control.

Methods

It is a retrospective study. Parameters of interest were obtained from the electronic medical records of 248 type 2 diabetes patients.

Results

The mean age of the patients was 59.6 ± 12.7 years. Biguanides (85.5%) and DPP-IV inhibitors (60.9%) were the most commonly used antidiabetic drugs, while thiazolidinediones (1.2%), α-glucosidase inhibitors (0.8%), and meglitinides (0%) were the least prescribed. In addition, SGLT-2 inhibitors, insulins, GLP-1 agonists, and sulphonylureas were prescribed to 31.0%, 17.1%, 15.3%, and 14.9%, respectively. On average, about 2-3 hypoglycaemic agents were prescribed. The number of drugs used and HbA1c were significantly negatively correlated. Biguanide, insulins, and DPP-IV inhibitors were associated with a significantly lower HbA1c.

Conclusions

Metformin is the drug of choice. Combining 2 to 3 drugs is very common. Glycaemic control is better in the present study, compared with prior studies, and it could be due to the pattern of drugs used in this study.
Combined cerebrospinal fluid metabolomic and cytokine profiling in tuberculosis meningitis reveals robust and prolonged changes in immunometabolic networks
Authors: Tomalka Jeff , Sharma Ashish , Smith Alison G.C. , Avaliani Teona , Gujabidze Mariam , Bakuradze Tinatin , Sabanadze Shorena , Jones Dean P. , Avaliani Zaza , Kipiani Maia , Kempker Russell R. , Collins Jeffrey M.
Published: June 2023

Much of the high mortality in tuberculosis meningitis (TBM) is attributable to excessive inflammation, making it imperative to identify targets for host-directed therapies that reduce pathologic inflammation and mortality. In this study, we investigate how cytokines and metabolites in the cerebral spinal fluid (CSF) associate with TBM at diagnosis and during TBM treatment. At diagnosis, TBM patients demonstrate significant increases versus controls of cytokines and chemokines that promote inflammation and cell migration including IL-17A, IL-2, TNFα, IFNγ, and IL-1β. Inflammatory immune signaling was strongly correlated with immunomodulatory metabolites including kynurenine, lactic acid, carnitine, tryptophan, and itaconate. Inflammatory immunometabolic networks were only partially reversed with two months of effective TBM treatment and remained significantly different versus control CSF. Together, these data highlight a critical role for host metabolism in regulating the inflammatory response to TBM and indicate the timeline for restoration of immune homeostasis in the CSF is prolonged.
The brain’s sensitivity to sensory error can be modulated by altering perceived variability
Authors: Tang Ding-lan , Parrell Benjamin , Beach Sara D. , Niziolek Caroline A.
Published: June 2023

When individuals make a movement that produces an unexpected outcome, they learn from the resulting error. This process, essential in both acquiring new motor skills and adapting to changing environments, critically relies on error sensitivity, which governs how much behavioral change results from a given error. Although behavioral and computational evidence suggests error sensitivity can change in response to task demands, neural evidence regarding the flexibility of error sensitivity in the human brain is lacking. Critically, the sensitivity of the nervous system to auditory errors during speech production, a complex and well-practiced motor behavior, has been extensively studied by examining the prediction-driven suppression of auditory cortical activity. Here, we tested whether the nervous system’s sensitivity to errors, as measured by this suppression, can be modulated by altering speakers’ perceived variability. Our results showed that error sensitivity was increased after exposure to an auditory perturbation that increased participants’ perceived variability, consistent with predictions generated from previous behavioral data and state-space modeling. Conversely, we observed no significant changes in error sensitivity when perceived variability was unaltered or artificially reduced. The current study establishes the validity of behaviorally modulating the nervous system’s sensitivity to errors. As sensitivity to sensory errors plays a critical role in sensorimotor adaptation, modifying error sensitivity has the potential to enhance motor learning and rehabilitation in speech and, potentially, more broadly across motor domains.

Significance Statement

The process of learning from error is essential for both the acquisition of new skills and successful adaptation to changing environments. Such error-based learning critically relies on error sensitivity, which determines how much we learn from a given error. Although evidence from behavioral studies suggests error sensitivity is malleable, neural evidence regarding the flexibility of error sensitivity in the human brain is lacking. Here, we showed that the nervous system’s sensitivity to errors can be modulated by altering perceived variability. The present study establishes the validity of behaviorally modulating neural sensitivity to sensory errors. Improving our ability to learn from error can play a critical role in applied settings such as rehabilitation.
ASSAY OF THE DREEM DEVICE ON SLEEP METRICS AND AN EXPLORATION OF SLEEP STAGING IN CHRONIC SHORT SLEEPERS DURING TIME IN BED EXTENSION
Authors: Mallender Zachary , Depner Christopher M.
Published: June 2023

Despite clear research findings showing that sleeping less than seven hours per night has an array of health consequences, over 1 in 3 American adults report sleeping less than seven hours per night. Many studies exploring the consequences of insufficient sleep are restricted to small sample sizes and are of relatively short duration due to a significant cost of gold-standard polysomnography in terms of participant burden, expense, time, and reliance on trained sleep technicians. Additionally, many studies of short sleep duration use a paradigm of experimental sleep restriction on otherwise healthy sleepers, which excludes people who chronically obtain short sleep duration over months to years. Here, we explore possible solutions to these issues by implementing a sleep extension protocol in 14 adults (average age 20.6±2.5y; +/- SD) with self-reported habitual sleep duration less than 6.5h/night. Participants completed 2 weeks of baseline monitoring (habitual short sleep duration) and then were instructed to increase time in bed to ≥8h/night for four weeks. Sleep was monitored using wrist-actigraphy and the Dreem 2 headband, a wireless dry electrode consumer electroencephalography (EEG) device. Compared to wrist-actigraphy, the Dreem 2 shows minimal systemic skew for nights with data quality over 75% (as assigned by the Dreem algorithm). However, Bland Altman analysis shows significant random error with limits of agreement approximately +/- 70 minutes between actigraphy and the Dreem. Exploration of sleep metrics from the Dreem 2 during baseline short sleep versus sleep extension revealed an increase in total sleep time; increase in all recorded sleep stages; and no significant changes in sleep onset latency, wakefulness after sleep onset, or sleep efficiency. Although several limitations of producing high quality data were identified, the Dreem 2 headband shows promise as a home environment sleep research device. With an improvement in data quality, the Dreem headband, or another wireless consumer sleep device, has the potential to help advance the sleep field in ways that were previously inaccessible with clinical PSG.
An Epidemic Model for Multi-Intervention Outbreaks
Authors: Schaber Kathryn L. , Kumar Sagar , Lubwama Baker , Desai Angel , Majumder Maimuna S.
Published: June 2023

Modeling is an important tool to utilize at the beginning of an infectious disease outbreak, as it allows estimation of parameters—such as the basic reproduction number, R0—that can be used to postulate how the outbreak may continue to spread. However, there exist many challenges that need to be accounted for, such as an unknown first case date, retrospective reporting of ‘probable’ cases, changing dynamics between case count and death count trends, and the implementation of multiple control efforts and their delayed or diminished effects. Using the near-daily data provided from the recent outbreak of Sudan ebolavirus in Uganda as a case study, we create a model and present a framework aimed at overcoming these aforementioned challenges. The impact of each challenge is examined by comparing model estimates and fits throughout our framework. Indeed, we found that allowing for multiple fatality rates over the course of an outbreak generally resulted in better fitting models. On the other hand, not knowing the start date of an outbreak appeared to have large and non-uniform effects on parameter estimates, particularly at the beginning stages of an outbreak. While models that did not account for the decaying effect of interventions on transmission underestimated R0, all decay models run on the full dataset yielded precise R0estimates, demonstrating the robustness of R0as a measure of disease spread when examining data from the entire outbreak.
Automated Extraction and Visualization of Metabolic Networks from Biomedical Literature Using a Large Language Model
Authors: Phongwattana Thiptanawat , Chan Jonathan H
Published: June 2023

The rapid growth of biomedical literature presents a significant challenge for researchers to extract and analyze relevant information efficiently. In this study, we explore the application of GPT, the large language model to automate the extraction and visualization of metabolic networks from a corpus of PubMed abstracts. Our objective is to provide a valuable tool for biomedical researchers to explore and understand the intricate metabolic interactions discussed in scientific literature. We begin by splitting a ton of the tokens within the corpus, as the GPT-3.5-Turbo model has a token limit of 4,000 per analysis. Through iterative prompt optimization, we successfully extract a comprehensive list of metabolites, enzymes, and proteins from the abstracts. To validate the accuracy and completeness of the extracted entities, our biomedical data domain experts compare them with the provided abstracts and ensure a fully matched result. Using the extracted entities, we generate a directed graph that represents the metabolic network including 3 types of metabolic events that consist of metabolic consumption, metabolic reaction, and metabolic production. The graph visualization, achieved through Python and NetworkX, offers a clear representation of metabolic pathways, highlighting the relationships between metabolites, enzymes, and proteins. Our approach integrates language models and network analysis, demonstrating the power of combining automated information extraction with sophisticated visualization techniques. The research contributions are twofold. Firstly, we showcase the ability of GPT-3.5-Turbo to automatically extract metabolic entities, streamlining the process of cataloging important components in metabolic research. Secondly, we present the generation and visualization of a directed graph that provides a comprehensive overview of metabolic interactions. This graph serves as a valuable tool for further analysis, comparison with existing pathways, and updating or refining metabolic networks. Our findings underscore the potential of large language models and network analysis techniques in extracting and visualizing metabolic information from scientific literature. This approach enables researchers to gain insights into complex biological systems, advancing our understanding of metabolic pathways and their components.
Genetically-encoded probes to determine nonspecific hydrophobic and electrostatic binding in cells
Authors: Zuo Weiyan , Huang Meng-Ruo , Schmitz Fabian , Boersma Arnold J.
Published: June 2023

Proteins interact nonspecifically with other components in the crowded cell through associative interactions. This environmental stickiness alters for example folding stability, protein diffusion, and aggregation propensity. However, the magnitude and variation in nonspecific electrostatic and hydrophobic binding energies in the cell are unclear. Here, we develop genetically-encoded fluorescence excitation ratiometric probes to determine nonspecific binding interactions. We determine hydrophobic and electrostatic interactions by systematically varying a sensing peptide on the probe. The sensors are verified in vitro and tested in HEK293T, where the nonspecific binding is highest for highly cationic and hydrophobic domains. Perturbing the cell by energy depletion increases the dependence of binding strength on peptide electrostatics, showing that the cellular conditions tune the nonspecific interaction architecture in cells. The sensors will allow estimation of nonspecific interactions and how these interactions may change in response to stresses.
Encoding manual dexterity through modulation of intrinsic alpha band connectivity
Authors: Maddaluno O. , Della Penna S. , Pizzuti A. , Spezialetti M. , Corbetta M. , de Pasquale F. , Betti V.
Published: June 2023

Using hands proficiently implies consolidated motor skills, yet malleable to task demands. How the brain realizes this balance between stability and flexibility is unknown. At rest, in absence of overt input or behavior, the communication within the brain may represent a neuralpriorof stored memories. This magnetoencephalography study addresses how the modulation of such stable connectivity, induced by motor tasks, relates to proficient behavior. To this aim, we estimated functional connectivity from 51 participants of the Human Connectome Project during rest and finger tapping in alpha and beta bands. We identified two groups of participants characterized by opposite patterns of connectivity strength and topology.High and low performersshowed distributed decreases and increases of connectivity, respectively. However, while dexterous individuals also show modulations of the motor network,low performersexhibited a stability of such connections. Furthermore, in dexterous individuals, an increased segregation was observed through an increment of network modularity and decrease of nodal centrality. Instead,low performersshow a dysfunctional increased integration. Our findings reveal that the balance between stability and flexibility is not fixed; rather it constrains proficient behavior.
Neural representation dynamics reveal computational principles of cognitive task learning
Authors: Mill Ravi D. , Cole Michael W.
Published: June 2023

During cognitive task learning, neural representations must be rapidly constructed for novel task performance, then optimized for robust practiced task performance. How the geometry of neural representations changes to enable this transition from novel to practiced performance remains unknown. We hypothesized that practice involves a shift from compositional representations (task-general activity patterns that can be flexibly reused across tasks) to conjunctive representations (task-specific activity patterns specialized for the current task). Functional MRI during learning of multiple complex tasks substantiated this dynamic shift from compositional to conjunctive representations, which was associated with reduced cross-task interference (via pattern separation) and behavioral improvement. Further, we found that conjunctions originated in subcortex (hippocampus and cerebellum) and slowly spread to cortex, extending multiple memory systems theories to encompass cognitive task learning. The strengthening of conjunctive representations hence serves as a computational signature of learning, reflecting cortical-subcortical dynamics that optimize task representations in the human brain.

Highlights

Learning shifts multi-task representations from compositional to conjunctive formats

Cortical conjunctions uniquely associate with improved behavior and pattern separation

These conjunctions strengthen over separated learning events and index switch costs

Subcortical regions are critical for cross-region binding of task rule information
Uncovering Language Disparity of ChatGPT in Healthcare: Non-English Clinical Environment for Retinal Vascular Disease Classification
Authors: Liu Xiaocong , Wu Jiageng , Shao An , Shen Wenyue , Ye Panpan , Wang Yao , Ye Juan , Jin Kai , Yang Jie
Published: June 2023

Objective

To evaluate the effectiveness and reasoning ability of ChatGPT in diagnosing retinal vascular diseases in the Chinese clinical environment.

Materials and Methods

We collected 1226 fundus fluorescein angiography reports and corresponding diagnosis written in Chinese, and tested ChatGPT with four prompting strategies (direct diagnosis or diagnosis with explanation and in Chinese or English).

Results

ChatGPT using English prompt for direct diagnosis achieved the best performance, with F1-score of 80.05%, which was inferior to ophthalmologists (89.35%) but close to ophthalmologist interns (82.69%). Although ChatGPT can derive reasoning process with a low error rate, mistakes such as misinformation (1.96%), and hallucination (0.59%) still exist.

Discussion and Conclusions

ChatGPT can serve as a helpful medical assistant to provide diagnosis under non-English clinical environments, but there are still performance gaps, language disparity, and errors compared to professionals, which demonstrates the potential limitations and the desiration to continually explore more robust LLMs in ophthalmology practice.
Knowledge of COVID-19 prevention in Eastern Ethiopia
Authors: Dheresa Merga , Madewell Zachary J. , Muir Jonathan A. , Getachew Tamirat , Daraje Gamachis , Mengesha Gezahegn , Whitney Cynthia G. , Assefa Nega , Cunningham Solveig A.
Published: June 2023

Objectives

As of May 2023, over 500,000 COVID-19 cases and over 7,500 deaths have been reported in Ethiopia. Understanding community members’ knowledge and perception of SARS-CoV-2 prevention is essential for directing public health interventions to reduce transmission and improve vaccination coverage. Here, we aimed to describe factors associated with knowledge of COVID-19 prevention among community residents in Eastern Ethiopia.

Methods

We conducted a cross-sectional survey among a random sample of 880 participants in a Health and Demographic Surveillance System in the Harari Region, Ethiopia, from August to September 2021. Principal components analysis was used to create a score representing knowledge of COVID-19 prevention. Quasi-Poisson regression was used to examine associations between demographic characteristics and knowledge of COVID-19 prevention. Our survey also included information regarding knowledge of community or government measures to prevent COVID-19, healthcare services for children under five, and healthcare services for pregnant women.

Results

The most cited individual measures to reduce the risk of contracting COVID-19 were washing hands with soap (91.5%) and wearing a facemask (89.2%), whereas least mentioned were avoiding domestic and international travel (22.2%) and wearing medical gloves (20.3%). The most recognized community or government measures to prevent SARS-CoV-2 transmission were closure of schools and universities (77.0%), advice to avoid gatherings (75.2%), and advice to stay home (62.3%). Adjusted analyses demonstrated that knowledge of COVID-19 prevention was higher among participants from rural areas than urban areas, those aged ≥65 years (<25 years as reference), with secondary education (no formal education as reference), with monthly income of ≥2,001 Birr (0-1,200 as reference), and were farmers or domestic/subsistence workers or government employees (unemployed as reference). Knowledge was lower among households with ≥5 household members (1-2 as reference). Of households with children under five and pregnant women, 9.4% and 12.3% missed at least one medical care visit since mid-March 2020 consequent to the pandemic, respectively.

Conclusions

Public health interventions to reduce infectious disease transmission depend on perceptions of risk and knowledge. The survey found that most adults had good knowledge of methods for reducing risks of COVID-19, although knowledge differed between groups. A substantial number of respondents reported missing important healthcare visits. Understanding these factors may help Ethiopian authorities plan effective health education programs to control community and household transmission of SARS-CoV-2.
Neuronal Spike Shapes (NSS): A Straightforward Approach to Investigate Heterogeneity in Neuronal Excitability States
Authors: Martini Lorenzo , Amprimo Gianluca , Di Carlo Stefano , Olmo Gabriella , Ferraris Claudia , Savino Alessandro , Bardini Roberta
Published: June 2023

The mammalian brain exhibits a remarkable diversity of neurons, contributing to its intricate architecture and functional complexity. The analysis of multimodal single-cell datasets enables the investigation of cell types and states heterogeneity. In this study, we introduce the Neuronal Spike Shapes (NSS), a straightforward approach for the exploration of excitability states of neurons based on their Action Potential (AP) waveforms. The NSS method describes the AP waveform based on a triangular representation complemented by a set of derived electrophysiological (EP) features. To support this hypothesis, we validate the proposed approach on two datasets of murine cortical neurons, focusing it on GABAergic neurons. The validation process involves a combination of NSS-based clustering analysis, features exploration, Differential Expression (DE), and Gene Ontology (GO) enrichment analysis. Results show that the NSS-based analysis captures neuronal excitability states that possess biological relevance independently of cell subtype. In particular, Neuronal Spike Shapes (NSS) captures, among others, a well-characterized fast-spiking excitability state, supported by both electrophysiological and transcriptomic validation. Gene Ontology Enrichment Analysis reveals voltage-gated potassium (K+) channels as specific markers of the identified NSS partitions. This finding strongly corroborates the biological relevance of NSS partitions as excitability states, as the expression of voltage-gatedK+channels regulates the hyperpolarization phase of the AP, being directly implicated in the regulation of neuronal excitability.