Enhancing virtual screening enrichment has become an urgent problem in computational chemistry, driven by increasingly large databases of commercially available compounds, without a commensurate drop in in vitro screening costs. Docking these large databases is possible with cloud-scale computing. However, rapid docking necessitates compromises in scoring, often leading to poor enrichment and an abundance of false positives in docking results. This work describes a new scoring function composed of two parts - a knowledge-based component that predicts the probability of a particular atom type being in a particular receptor environment, and a tunable weight matrix that converts the probability predictions into a dimensionless score suitable for virtual screening enrichment. This score, the FitScore, represents the compatibility between the ligand and the binding site and is capable of a high degree of enrichment across standardized docking test sets.

BACKGROUND: ChatGPT (OpenAI), a state-of-the-art large language model, has exhibited remarkable performance in various specialized applications. Despite the growing popularity and efficacy of artificial intelligence, there is a scarcity of studies that assess ChatGPT\'s competence in addressing multiple-choice questions (MCQs) using KIDMAP of Rasch analysis-a website tool used to evaluate ChatGPT\'s performance in MCQ answering.
OBJECTIVE: This study aims to (1) showcase the utility of the website (Rasch analysis, specifically RaschOnline), and (2) determine the grade achieved by ChatGPT when compared to a normal sample.
METHODS: The capability of ChatGPT was evaluated using 10 items from the English tests conducted for Taiwan college entrance examinations in 2023. Under a Rasch model, 300 simulated students with normal distributions were simulated to compete with ChatGPT\'s responses. RaschOnline was used to generate 5 visual presentations, including item difficulties, differential item functioning, item characteristic curve, Wright map, and KIDMAP, to address the research objectives.
RESULTS: The findings revealed the following: (1) the difficulty of the 10 items increased in a monotonous pattern from easier to harder, represented by logits (-2.43, -1.78, -1.48, -0.64, -0.1, 0.33, 0.59, 1.34, 1.7, and 2.47); (2) evidence of differential item functioning was observed between gender groups for item 5 (P=.04); (3) item 5 displayed a good fit to the Rasch model (P=.61); (4) all items demonstrated a satisfactory fit to the Rasch model, indicated by Infit mean square errors below the threshold of 1.5; (5) no significant difference was found in the measures obtained between gender groups (P=.83); (6) a significant difference was observed among ability grades (P<.001); and (7) ChatGPT\'s capability was graded as A, surpassing grades B to E.
CONCLUSIONS: By using RaschOnline, this study provides evidence that ChatGPT possesses the ability to achieve a grade A when compared to a normal sample. It exhibits excellent proficiency in answering MCQs from the English tests conducted in 2023 for the Taiwan college entrance examinations.

UNASSIGNED: Bedside lung ultrasonography has been widely used in neonatal intensive care units (NICUs). Lung ultrasound scores (LUS) may predict the need for pulmonary surfactant (PS) application. PS replacement therapy is the key intervention for managing moderate to severe neonatal respiratory distress syndrome (NRDS), with early PS administration playing a positive role in improving patient outcomes. Lung ultrasonography aids in the prompt diagnosis of NRDS, while LUS offers a semi-quantitative assessment of lung health. However, the specific methodologies for utilizing LUS in clinical practice remain controversial. This study hypothesizes that, in very preterm infants [<32 weeks gestational age (GA)] exhibiting respiratory distress symptoms, determining PS application through early postnatal LUS combined with clinical indicators, as opposed to relying solely on clinical signs and chest x-rays, can lead to more timely PS administration, reduce mechanical ventilation duration, improve patient outcomes, and lower the occurrence of bronchopulmonary dysplasia (BPD).
UNASSIGNED: This is a protocol for a prospective, non-blinded, randomized controlled trial that will be conducted in the NICU of a hospital in China. Eligible participants will include very preterm infants (< 32 weeks GA) exhibiting signs of respiratory distress. Infants will be randomly assigned in a 1:1 ratio to either the ultrasound or control group. In the ultrasonography group, the decision regarding PS administration will be based on a combination of lung ultrasonography and clinical manifestations, whereas in the control group, it will be determined solely by clinical signs and chest x-rays. The primary outcome measure will be the mechanical ventilation duration. Statistical analysis will employ independent sample t-tests with a significance level set at α = 0.05 and a power of 80%. The study requires 30 infants per group (in total 60 infants).
UNASSIGNED: This study aims to demonstrate that determining PS application based on a combination of LUS and clinical indicators is superior to traditional approaches.
UNASSIGNED: This approach may enhance the accuracy of NRDS diagnosis and facilitate early prediction of PS requirements, thereby reducing the duration of mechanical ventilation. The findings of this research may contribute valuable insights into the use of LUS to guide PS administration.

BACKGROUND: Numerous states have introduced cardiopulmonary resuscitation (CPR) training mandates for high school students and staff to prevent sudden cardiac death (SCD). However, the content and implementation of these mandates vary substantially. Furthermore, a comprehensive and objective assessment of these mandates and their impact is lacking.
OBJECTIVE: To conduct a thorough evaluation of CPR training mandates across the United States.
METHODS: We developed a novel scoring system based on proposed CPR standards, training and certification requirements, and legislative action to assess current mandates. This was used to rate the CPR mandates across all 50 states and the District of Columbia. Mandate scores were then compared to available real-world registry data as a surrogate for efficacy from 2018 to 2021.
RESULTS: State CPR mandate scores ranged from 0 to 47, with a higher score indicating more robust mandates. The median and mean scores were 24 [IQR 19.5-27] and 21.52±8.61, respectively, with 35 being the highest score. Intra-observer variability was 0.986 (95% CI 0.944-1.028; p<0.001). The year of implementation did not influence the strength of the score (R2=-0.173; 95% CI -0.447-0.131, p=0.262), Correlation between SCD rate (R2=-0.76; 95% CI -0.492-0.367, p=0.742), bystander-initiated CPR (R2= -0.006; 95% CI -0.437-0.427, p=0.978), automatic external defibrillator use (R2= -0.125; 95% CI -0.528-0.324, p=0.590), or cardiovascular death rate (R2=-0.13; 95% CI -0.379-0.21, p=0.355) failed to reach statistical significance.
CONCLUSIONS: Modest scoring consistency highlights the need for robust, standardized CPR requirements to potentially mitigate SCD. This study lays the groundwork for evidence-informed policy development in this area.

Same-day emergency care (SDEC) in unplanned and emergency care is an NHS England (NHSE) priority. Optimal use of these services requires rapid identification of suitable patients. NHSE suggests the use of one tool for this purpose. This systematic review compares studies that evaluate the performance of selection tools for SDEC pathways. Nine studies met the inclusion criteria. Three scores were evaluated: the Amb score (seven studies), Glasgow Admission Prediction Score (GAPS) (six studies) and Sydney Triage to Admission Risk Tool (START) (two studies). There was heterogeneity in the populations assessed, exclusion criteria used and definitions used for SDEC suitability, with proportions of patients deemed \'suitable\' for SDEC ranging from 20 to 80%. Reported score sensitivity and specificity ranged between 18-99% and 10-89%. Score performance could not be compared due to heterogeneity between studies. No studies assessed clinical implementation. The current evidence to support the use of a specific tool for SDEC is limited and requires further evaluation.

UNASSIGNED: Organophosphate compounds (OPCs) pose significant health risks, especially in developing countries with limited resources. Predicting outcomes in OPCs poisoning is crucial for guiding clinical management and reducing mortality rates. The aim of this study to evaluate the validity of different scoring systems Rapid Emergency Medicine Score, Multiple Organ Dysfunction Score, Acute Physiology and Chronic Health Evaluation Score, and Poison Severity Score in prediction of intensive care unit (ICU) admission and mortality of acute OPCs poisoning patients.
UNASSIGNED: A cross-sectional study was conducted on 103 patients admitted to Xx Poison Control Center between May 2022 and June 2023. Scoring systems were applied at admission, and their performance in predicting the need for ICU admission and mortality was evaluated using receiver operating characteristic (ROC) curve analysis.
UNASSIGNED: Most patients survived (92.2%). Only 13.6% of the patients required ICU admission. Significant differences in median scores were observed between survivors and non-survivors and between patients requiring ICU admission and those who did not. Multiple Organ Dysfunction Score exhibited the highest discriminatory power for predicting both ICU admission (AUC = 0.983) and mortality (AUC = 0.999).
UNASSIGNED: The findings highlight the importance of utilizing scoring systems, particularly Multiple organ dysfunction score, for prediction of poor outcomes of acute OPCs poisoning.

BACKGROUND: Several classifications have been reported to stratify non-muscle-invasive bladder cancer (NMIBC) in risk groups according to the probability of recurrence and progression.
OBJECTIVE: To systematically review the current evidence regarding risk stratification of NMIBC.
METHODS: The systematic review was performed in accordance with the PRISMA statement. Studies providing data on development and/or external validation cohorts of models and risk stratification tables for recurrence and/or progression for patients with NMIBC, reporting at least one discrimination measure (AUC or C-Index) were included.
RESULTS: Twenty-five studies involving 22,737 patients were included. Six classifications were identified, three of them were predictive models (EORTC, CUETO, EAU 2021) and three were based on expert opinion (EAU 2020, AUA, NCCN). A high risk of bias was present in the majority of the studies. Certain heterogenicity was found among the studies regarding adjuvant therapy, postoperative instillation or second resection. The definition of oncological outcomes was not standardized in the included studies. CUETO and EORTC scoring systems are the most validated. In general, validations showed a poor discrimination capability to predict recurrence, slightly better for progression. The EAU 2021 model overestimates the risk of progression in patients treated with BCG. Carcinoma in situ is underrepresented in all the studies analyzed.
CONCLUSIONS: The existing classifications show poor discrimination capability for recurrence and possibly helpful discrimination capability for progression in NMIBC patients. These results highlight the unmet need to develop novel accurate risk models for patients with NMIBC, which could be improved with the combination of clinicopathological and molecular information.

UNASSIGNED: Ovarian cancer is a deadly women cancer with many chemoresistance after standard treatment. Ovarian cancer tissues\' CD44+/CD24- (CSCs), RAD6 overexpression and DDB2 underexpression are associated with chemoresistance, recurrence, and poor prognosis of the disease because of the existence of cancer stem cells (CSCs). We tried to analyze the expression of those three proteins while building a predictor scoring system to predict the ovarian cancer chemoresistance from the ovarian cancer tissue immunohistochemistry.
UNASSIGNED: We conducted a cohort study of 64 patients divided into two groups (32 patients in each group) at the Cipto Mangunkusumo, Tarakan, Dharmais, and Fatmawati Hospital which are located in Jakarta city, Indonesia. The patients underwent cytoreductive debulking and histopathological examination continued by six series of chemotherapy followed by six months of observation. We divided the groups into chemoresistant and chemosensitive by using Response Criteria in Solid Tumors (RECIST) criteria. Ovarian cancer tissue immunohistochemistry tests were then performed to count the CSCs, RAD6 and DDB2 expressions.
UNASSIGNED: We found relationship between increased CSCs, RAD6 and reduced DDB2 (p < 0.05) expression in ovarian cancer tissue with the chemoresistance. A possible predictor scoring system named IHC-UNEDO scoring was built to aid the ovarian cancer chemoresistance prediction.
UNASSIGNED: The conclusion is that CSCs, RAD6 and DDB2 expressions are significantly associated with ovarian cancer chemoresistance, and IHC-UNEDO scoring should be considered as a tool to predict ovarian cancer chemoresistance.

UNASSIGNED: Multiple-choice examinations are frequently used in German dental schools. However, details regarding the used item types and applied scoring methods are lacking.
UNASSIGNED: This study aims to gain insight into the current use of multiple-choice items (ie, questions) in summative examinations in German undergraduate dental training programs.
UNASSIGNED: A paper-based 10-item questionnaire regarding the used assessment methods, multiple-choice item types, and applied scoring methods was designed. The pilot-tested questionnaire was mailed to the deans of studies and to the heads of the Department of Operative/Restorative Dentistry at all 30 dental schools in Germany in February 2023. Statistical analysis was performed using the Fisher exact test (P<.05).
UNASSIGNED: The response rate amounted to 90% (27/30 dental schools). All respondent dental schools used multiple-choice examinations for summative assessments. Examinations were delivered electronically by 70% (19/27) of the dental schools. Almost all dental schools used single-choice Type A items (24/27, 89%), which accounted for the largest number of items in approximately half of the dental schools (13/27, 48%). Further item types (eg, conventional multiple-select items, Multiple-True-False, and Pick-N) were only used by fewer dental schools (≤67%, up to 18 out of 27 dental schools). For the multiple-select item types, the applied scoring methods varied considerably (ie, awarding [intermediate] partial credit and requirements for partial credit). Dental schools with the possibility of electronic examinations used multiple-select items slightly more often (14/19, 74% vs 4/8, 50%). However, this difference was statistically not significant (P=.38). Dental schools used items either individually or as key feature problems consisting of a clinical case scenario followed by a number of items focusing on critical treatment steps (15/27, 56%). Not a single school used alternative testing methods (eg, answer-until-correct). A formal item review process was established at about half of the dental schools (15/27, 56%).
UNASSIGNED: Summative assessment methods among German dental schools vary widely. Especially, a large variability regarding the use and scoring of multiple-select multiple-choice items was found.

BACKGROUND: Progress in advancing sleep research employing polysomnography (PSG) has been negatively impacted by the limited availability of widely available, open-source sleep-specific analysis tools.
METHODS: Here, we introduce Counting Sheep PSG, an EEGLAB-compatible software for signal processing, visualization, event marking and manual sleep stage scoring of PSG data for MATLAB.
RESULTS: Key features include: (1) signal processing tools including bad channel interpolation, down-sampling, re-referencing, filtering, independent component analysis, artifact subspace reconstruction, and power spectral analysis, (2) customizable display of polysomnographic data and hypnogram, (3) event marking mode including manual sleep stage scoring, (4) automatic event detections including movement artifact, sleep spindles, slow waves and eye movements, and (5) export of main descriptive sleep architecture statistics, event statistics and publication-ready hypnogram.
METHODS: Counting Sheep PSG was built on the foundation created by sleepSMG (https://sleepsmg.sourceforge.net/). The scope and functionalities of the current software have made significant advancements in terms of EEGLAB integration/compatibility, preprocessing, artifact correction, event detection, functionality and ease of use. By comparison, commercial software can be costly and utilize proprietary data formats and algorithms, thereby restricting the ability to distribute and share data and analysis results.
CONCLUSIONS: The field of sleep research remains shackled by an industry that resists standardization, prevents interoperability, builds-in planned obsolescence, maintains proprietary black-box data formats and analysis approaches. This presents a major challenge for the field of sleep research. The need for free, open-source software that can read open-format data is essential for scientific advancement to be made in the field.