Model-based Results Research Articles

Development of Analytical Model to Describe Reflectance Spectra in Leaves with Palisade and Spongy Mesophyll.

Remote sensing plays an important role in plant cultivation and ecological monitoring. This sensing is often based on measuring spectra of leaf reflectance, which are dependent on morphological, biochemical, and physiological characteristics of plants. However, interpretation of the reflectance spectra requires the development of new tools to analyze relations between plant characteristics and leaf reflectance. The current study was devoted to the development, parameterization, and verification of the analytical model to describe reflectance spectra of the dicot plant leaf with palisade and spongy mesophyll layers (on the example of pea leaves). Four variables (intensities of forward and backward collimated light and intensities of forward and backward scattered light) were considered. Light reflectance and transmittance on borders of lamina (Snell's and Fresnel's laws), light transmittance in the palisade mesophyll (Beer-Bouguer-Lambert law), and light transmittance and scattering in the spongy mesophyll (Kubelka-Munk theory) were described. The developed model was parameterized based on experimental results (reflectance spectra, contents of chlorophylls and carotenoid, and thicknesses of palisade and spongy mesophyll in pea leaves) and the literature data (final R2 was 0.989 for experimental and model-based reflectance spectra). Further model-based and experimental investigations showed that decreasing palisade and spongy mesophyll thicknesses in pea leaves (from 35.5 to 25.2 µm and from 58.6 to 47.8 µm, respectively) increased reflectance of green light and decreased reflectance of near-infrared light. Similarity between model-based and experimental results verified the developed model. Thus, the model can be used to analyze leaf reflectance spectra and, thereby, to increase efficiency of the plant remote and proximal sensing.

Intelligent Diagnosis of Hypopigmented Dermatoses and Intelligent Evaluation of Vitiligo Severity on the Basis of Deep Learning.

There is a lack of objective, accurate, and convenient methods for classification diagnostic hypopigmented dermatoses (HD) and severity evaluation of vitiligo. To achieve an accurate and intelligent classification diagnostic model of HD and severity evaluation model of vitiligo using a deep learning-based method. A total of 11,483 images from 4744 patients with HD were included in this study. An optimal diagnostic model was constructed by merging the squeeze-and-excitation (SE) module with the candidate model, its diagnostic efficiency was compared with that of 98 dermatologists. An objective severity evaluation indicator was proposed through weighting method and combined with a segmentation model to form a severity evaluation model, which was then compared with the assessments conducted by three experienced dermatologists using the naked eye. The improved diagnosis model SE_ResNet-18 outperformed the other 11 classic models with an accuracy of 0.9389, macro-specificity of 0.9878, and macro-f1 score of 0.9395, and outperformed the different categories of 98 dermatologists (P < 0.001). The weighted Kappa test indicated medium consistency between the Indicatorv and the VASIchange (K = 0.567, P < 0.05). The optimal segmented model, HR-Net, had 0.8421 mIOU. The model-based severity evaluation results were not significantly different among the three experienced dermatologists. This study proposes an objective, accurate, and convenient hybrid model for diagnosing HD and evaluating the severity of vitiligo, providing a method for dermatologists especially in grassroots hospitals, and provides a foundation for telemedicine.

The Effect of Low-Dose CT Protocols on Shoulder Model-Based Tracking accuracy Using Biplane Videoradiography.

Model-based tracking is being increasingly used to quantify shoulder kinematics and typically employs computed tomography (CT) to create the 3D bone volumes, which adds to the total radiation exposure. Lower-dose CT protocols may be possible given the contrast between bone and the surrounding soft tissues. The purpose of this study was to describe the dose-accuracy tradeoff between low-dose CT scans and the kinematic tracking accuracy of the humerus, scapula, and clavicle when tracked using an intensity-based registration algorithm. Three fresh-frozen cadavers consisting of the torso and bilateral shoulders were tested. The CT protocols investigated included a full-dose protocol and 4 experimental low-dose protocols that modulated x-raytube current and peak voltage. Bead-based tracking (i.e., radiostereometric analysis) served as the reference standard to which model-based tracking results were compared. Accuracy was described in terms of both segmental (humerus, scapula, and clavicle) and joint (glenohumeral, acromioclavicular) kinematics using root-mean-square (RMSE), bias, precision, and worst-case errors. The low-dose CT scans resulted in an average dose reduction of 70.6-92.8%. RMSEs tended to increase as CT dose decreased with average glenohumeral errors increasing from 0.5° and 0.6 mm to 0.6° and 0.6 mm between the highest and lowest-dose protocols, and average acromioclavicular errors increasing from 0.6° and 0.8 mm to 0.7° and 0.9 mm. However, the difference in joint kinematic errors between the highest and lowest-dose CT scanning protocols was generally small (≤0.3°, ≤ 0.1 mm). It is possible to substantially reduce the CT dose associated with shoulder motion analysis using biplane videoradiography without significantly impacting data fidelity.

Online retail nudges to help parents with lower-income choose healthy beverages for their children: A randomized clinical trial.

Nudges offer a promising tool to reduce sugary drink intake among children who are most at risk for diet-related disease. To examine the impact of online store nudges on purchases of sugary drinks for children in lower-income households. Caregivers with lower-income were recruited to an online shopping experiment and instructed to spend $10-$30 on three beverages for their child aged 1-5 years. Participants were randomized to navigate an online supermarket in its standard version (n = 1106) or a version with nudges (n = 1135), including a product placement nudge (i.e. placing healthy beverages in prominent positions) and a swap nudge (i.e. offering a swap of water, plain milk and/or 100% fruit juice upon selection of sugary drinks). On average, participants purchased 1887 (SD = 2113) and 620 (SD = 1528) calories from sugary drinks per basket in the control and experimental conditions, respectively. Model-based results indicate that those in the experimental condition purchased 1267 (95% CI: 1419, 1114) fewer calories from sugary drinks, and fewer grams of total sugar (β = -253.5 g (95% CI: -286.3, -220.6)) and added sugar (β = -287.8 g (95% CI: -323.1, -252.5)) purchased from sugary drinks. Nudges may be an effective, acceptable, scalable strategy for leading caregivers in lower-income households to purchase fewer sugary drinks for their children.

Mathematical modeling of routine biological parameters kinetics in order to predict durable benefit for first-line immunotherapy in metastatic or unresectable melanoma.

e21549 Background: PD1, CTLA4 and LAG3 blockade have shown remarkable efficacy in melanoma patients. Predictive biomarkers are needed to predict response, prevent unneeded toxicity and reduce costs. Despite collective efforts, no biomarker is robust or accessible enough for routine use. Our objective was to evaluate mathematical modeling-based kinetic markers derived from routine biological parameters as early predictors of durable benefit for first-line immunotherapy in advanced melanoma. Our secondary objective was to assess if these modeling could predict progression-free survival (PFS). Methods: We retrospectively included all consecutive, treatment-naïve, metastatic or unresectable melanoma patients treated with PD1 blockade alone or in combination with CTLA4 or LAG3 blockade between April 2020 and 2022 in our department. Clinical, biological and radiological data were collected. The primary endpoint was durable clinical benefit (DCB) defined as partial or complete response at 6 months or stable disease of at least 6 months. 11 simple blood markers and their on-treatment kinetics were considered. 4 empirical mathematical models (constant, linear, double exponential and hyperbolic) were fitted to the data, allowing to reject the constant null hypothesis for all of them. The association of the best-models coefficients with DCB and PFS was assessed using logistic and Cox regression. To do so, the data was truncated at 1, 2 and 3 months and the model coefficients were retrieved using Bayesian estimation with a population prior identified on the full-kinetics dataset. This study was approved by our local IRB. Results: 61 patients were included with 24 months median follow-up. DCB was observed for 57% of them. A low model-based value at baseline using 1 month truncated data was significantly associated with DCB for neutrophils (OR = 4.54[1.69-12.5], p &lt; 0.0001) and - neutrophils/lymphocytes ratio (NLR) (OR = 3.03 [1.47-6.67], p = 0.003). Model-based results increased AUC versus baseline raw data: 0.755 versus 0.699 for neutrophils and 0.76 versus 0.7 for NLR. An elevated model-based at baseline using 1 month truncated data was significantly associated with PFS for neutrophils (HR = 1.56 [1.25-1.96], p &lt; 0.0001), AST (HR = 1.71 [1.24-2.35], p &lt; 0.0001), LDH (HR = 2.0 [1.44-2.77], p &lt; 0.0001), CPR (HR = 1.75 [1.14-2.67], p = 0.01) and ALT (HR = 1.37 [1.05-1.78], p = 0.02). At 1 month, the kinetics parameters on creatinine (HR = 1.38 [1.05-1.82], p = 0.02), albumin (HR = 0.68 [0.48-0.97], p = 0.03) and monocytes (HR = 1.49 [1.02-2.18], p = 0.04) were significantly associated with PFS. Conclusions: Modeling the kinetics of routine biological parameters shows potential ability to predict DCB. Perspectives include model improvement, kinetics machine learning integrative model development as well as a toxicity prediction.

Assessing environmental awareness activism to river decay and recurring pollution using parallel multiple mediator model

ABSTRACT The decay of rivers coupled with its escalated pollution is a concern worldwide. The present study intends to assess the agony of the riverine people due to the contamination of the Churni River and the transformation of their grievance into environmental activism at individual and community levels for rejuvenating the river and for a secured livelihood. The study observed an enriched concentration of biological oxygen demand, nitrate, and phosphate and a reduced concentration of dissolved oxygen during 2011–2020 due to the release of untreated industrial wastewater, sewage from urban areas, and agricultural runoff into the river. A systematic investigation reveals pollution-induced socio-psychological trauma and community awareness leading to the active involvement of the people in the environmental awareness movement, which was mediated by self-efficacy, self-responsibility, self-identity, and community value. A parallel multiple mediator model was employed, which included 29 parameters for exploring the key-mediated factors for the environmental movement. The model-based results identified that self-responsibility and community-value for upper stretch villages and self-efficacy and community-value for the lower stretch villages are significant mediators of environmental activism. A canonical component analysis predicted socio-demographic factors (e.g. age, dependency ratio female-male ratio, etc.) as intrinsically linked to environmental activism. This study is significant because it reveals how pollution and river degradation affect riverine people. It also clarifies how local grievances turned into environmental activism as a result of the documented socio-psychological trauma and growing community awareness. This study would thus play a pivotal role in understanding the human-nature complex to better planning and river restoration.

Examining Financial Hardship and Caregiver Subgroups in Kinship Foster Placements: A Machine Learning Approach

Children placed with kinship foster parents can experience less disruption and stronger family ties than children in non-kinship placements. However, financial hardship can restrict kinship caregivers from taking in relatives’ children. This study investigated (1) kinship caregivers’ financial standing compared to a national subsample of caregivers and (2) whether certain factors moderate the likelihood that a kinship caregiver will be able to provide care for additional non-relative children without additional financial assistance from the Department of Social Services (DSS). This study utilized primary data from 345 relatives across North Carolina and nationally representative secondary data on 6394 individuals’ financial circumstances. One-sample t-tests and chi-square goodness-of-fit tests revealed that caregivers who participated in our study generally fared better financially than caregivers at the national level. Model-based recursive partitioning results showed that if an additional child is placed in the home, the caregiver’s perceived capacity to provide care without extra DSS support decreased by approximately 19%, with a greater decrease (35%) among a subgroup of caregivers with low financial well-being status. The heterogeneity in caregivers’ experiences, capacities, and financial needs buttresses the need for nuanced interventions and programs targeting these caregivers, enabling them to provide more stable care for children placed in their homes.

Applications of land surface model to economic and environmental-friendly optimization of nitrogen fertilization and irrigation

Land surface models (LSMs) have prominent advantages for exploring the best agricultural practices in terms of both economic and environmental benefits with regard to different climate scenarios. However, their applications to optimizing fertilization and irrigation have not been well discussed because of their relatively underdeveloped crop modules. We used a CLM5-Crop LSM to optimize fertilization and irrigation schedules that follow actual agricultural practices for the cultivation of maize and wheat, as well as to explore the most economic and environmental-friendly inputs of nitrogen fertilizer and irrigation (FI), in the North China Plain (NCP), which is a typical intensive farming area. The model used the indicators of crop yield, farm gross margin (FGM), nitrogen use efficiency (NUE), water use efficiency (WUE), and soil nitrogen leaching. The results showed that the total optimal FI inputs of FGM were the highest (230 ± 75.8 kg N ha−1 and 20 ± 44.7 mm for maize; 137.5 ± 25 kg N ha−1 and 362.5 ± 47.9 mm for wheat), followed by the FIs of yield, NUE, WUE, and soil nitrogen leaching. After multi-objective optimization, the optimal FIs were 230 ± 75.8 kg N ha−1 and 20 ± 44.7 mm for maize, and 137.5 ± 25 kg N ha−1 and 387.5 ± 85.4 mm for wheat. By comparing our model-based diagnostic results with the actual inputs of FIs in the NCP, we found excessive usage of nitrogen fertilizer and irrigation during the current cultivation period of maize and wheat. The scientific collocation of fertilizer and water resources should be seriously considered for economic and environmental benefits. Overall, the optimized inputs of the FIs were in reasonable ranges, as postulated by previous studies. This result hints at the potential applications of LSMs for guiding sustainable agricultural development.

Minimum wage in Bangladesh: Awareness and impact a case of leather sector

This paper aims to explore the awareness of the minimum wage among workers in the tannery sector and assess its impact on poverty. The national minimum wage is a policy instrument for protecting workers’ rights. It has an effect on the employment, inequality, equity, and poverty of the workers. The effect is assessed using descriptive and econometric tools and techniques. The regression analysis is used to find the key social and economic determinants of poverty, along with the components of the minimum wage. In total, 120 workers are randomly chosen for interviews. The study shows that workers are less aware of the minimum wage, and 89% report the non-availability of the minimum wage. Nearly 55% of the workers’ families live below the poverty line. The Probit model-based regression results show that the implementation of bonuses and annual increments according to the minimum wage policy reduces the odds of poverty significantly. The presence of workers’ unions has no effect on the eradication of poverty, but household characteristics, particularly household size, are found to be a key factor in poverty. Sector experts consider non-compliance a governance failure in the tannery sector and suggest awareness campaigns, a reduction of job informality, and sanctions as deterrents to non-compliance.

Decoding the Dynamics of Climate Change Impact: Temporal Patterns of Surface Warming and Melting on the Nivlisen Ice Shelf, Dronning Maud Land, East Antarctica

This study analyzes the dynamics of surface melting in Antarctica, which are crucial for understanding glacier and ice sheet behavior and monitoring polar climate change. Specifically, we focus on the Nivlisen ice shelf in East Antarctica, examining melt ponds, supra glacial lakes (SGLs), seasonal surface melt extent, and surface ice flow velocity. Spatial and temporal analysis is based on Landsat and Sentinel-1 data from the austral summers of 2000 to 2023. Between 2000 and 2014, melt ponds and SGLs on the ice shelf covered roughly 1 km2. However, from 2015 to 2023, surface melting increased consistently, leading to more extensive melt ponds and SGLs. Significant SGL depths were observed in 2016, 2017, 2019, and 2020, with 2008, 2016, and 2020 showing the highest volumes and progressive SGL area growth. We also examined the relationship between seasonal surface melt extent and ice flow velocity. Validation efforts involved ground truth data from a melt pond in central Dronning Maud Land (cDML) during the 2022–2023 austral summer, along with model-based results. The observed increase in melt pond depth and volume may significantly impact ice shelf stability, potentially accelerating ice flow and ice shelf destabilization. Continuous monitoring is essential for accurately assessing climate change’s ongoing impact on Antarctic ice shelves.

Insights into the Peroxide-Bicyclic Intermediate Pathway of Aromatic Photooxidation: Experimental Yields and NOx-Dependency of Ring-Opening and Ring-Retaining Products.

Aromatic hydrocarbons are important contributors to the formation of ozone and secondary organic aerosols in urban environments. The different parallel pathways in aromatic oxidation, however, remain inadequately understood. Here, we investigated the production yields and chemical distributions of gas-phase tracer products during the photooxidation of alkylbenzenes at atmospheric OH levels with NOx present using high-resolution mass spectrometers. The peroxide-bicyclic intermediate pathway emerged as the major pathway in aromatic oxidation, accounting for 52.1 ± 12.6%, 66.1 ± 16.6%, and 81.4 ± 24.3% of the total OH oxidation of toluene, m-xylene, and 1,3,5-trimethylbenzene, respectively. Notably, the yields of bicyclic nitrates produced from the reactions of bicyclic peroxy radicals (BPRs) with NO were considerably lower (3-5 times) than what the current mechanism predicted. Alongside traditional ring-opening products formed through the bicyclic pathway (dicarbonyls and furanones), we identified a significant proportion of carbonyl olefinic acids generated via the 1,5-aldehydic H-shift occurring in subsequent reactions of BPRs + NO, contributing 4-7% of the carbon flow in aromatic oxidation. Moreover, the observed NOx-dependencies of ring-opening and ring-retaining product yields provide insights into the competitive nature of reactions involving BPRs with NO, HO2, and RO2, which determine the refined product distributions and offer an explanation for the discrepancies between the experimental and model-based results.

Seasonal and regional variability of model-based zooplankton biomass in the Salish Sea and evaluation against observations

We used a three-dimensional coupled biophysical model to examine zooplankton dynamics in the Salish Sea, NE Pacific. First, we evaluated the two zooplankton classes of the SalishSeaCast model using a transboundary zooplankton dataset comprised of observation data from the Canadian and United States waters of the Salish Sea from 2015 to 2019. Model zooplankton classes correspond to micro- and meso-zooplankton whose biomass is tightly coupled to phytoplankton through modelled food web dynamics (Z1) and mesozooplankton with life cycle-based seasonal grazing impacts (Z2). Overall, the model effectively captured seasonal patterns in observed biomass, although with slightly higher biomass estimates for both Z1 and Z2 (Bias = 0.10 and 0.08 g C m−2, respectively). Model fit varied regionally, with a weaker model fit being observed in nearshore regions. In addition, an autumn peak in Z2 was observed in the model, but not in the observations, suggesting some seasonal variations in model fit. Following the model evaluation, we used the model to determine seasonal and regional patterns of zooplankton grazing. Seasonally, the main peak in modelled zooplankton biomass increased in April or May in most of the regions defined within the Salish Sea and was driven by grazing on diatoms. Regionally, depth-integrated zooplankton biomass was consistently highest in areas adjacent to regions of strong tidal mixing. In addition, model-based zooplankton grazing was highest in the tidally mixed regions where phytoplankton biomass was high due to advection into the region despite low primary productivity. Our model-based results provide an opportunity to examine bottom-up food web processes at spatio-temporal scales not achievable with in situ sampling and help to elucidate key drivers of lower trophic level dynamics within the Salish Sea.

Identifying Rising Stars via Supervised Machine Learning

Identifying rising stars is very useful for faster growth of any organization. Rising entities has been explored in academics, sports, and blogs in the recent past, but business side is ignored. However, predicting rising business managers (RBMs) can result in significant business growth of any business. In order to maintain a competitive edge, machine learning techniques should be adopted to devise intelligent business strategies and perform predictions. In this article, RBMs are classified by exploring features of co-business managers (Co-BMs), rather than their own work history. Since ignoring their work history enables such prediction in a cold-start scenario, where work history is not available. After formulating features of Co-BM, the dataset is classified into two different evaluation setups. One is average revenue (AR) and the other one is average relative increase in revenue (ARIR)&#x2014;class labels. All instances for both labels are randomly sorted into multisize (10, 20, and 30&#x2013;100) datasets. Later on, these datasets are explored through machine learning classifiers using fivefold cross validation. In order to compare the prediction results with baseline and to measure the effectiveness of the proposed methods, the candidates&#x2019; business scores are used, which are calculated by the business definition and formulation. In terms of precision, recall, and f-measure, the feature, category, and model-based experimental results show that the generative models, particularly Bayesian networks, produce better results for an AR-based dataset. Also, overall results show the effective the proposed method.

Cost-Effectiveness Analysis for Therapy Sequence in Advanced Cancer: A Microsimulation Approach with Application to Metastatic Prostate Cancer.

Patients with advanced cancer may undergo multiple lines of treatment, switching therapies as their disease progresses. We developed a general microsimulation framework to study therapy sequence and applied it to metastatic prostate cancer. We constructed a discrete-time state transition model to study 2 lines of therapy. Using digitized published survival curves (progression-free survival, time to progression, and overall survival [OS]), we inferred event types (progression or death) and estimated transition probabilities using cumulative incidence functions with competing risks. We incorporated within-patient dependence over time; first-line therapy response informed subsequent event probabilities. Parameters governing within-patient dependence calibrated the model-based results to a target clinical trial. We applied these methods to 2 therapy sequences for metastatic prostate cancer, wherein both docetaxel (DCT) and abiraterone acetate (AA) are appropriate for either first- or second-line treatment. We assessed costs and quality-adjusted life-years (5-y QALYs) for 2 treatment strategies: DCT → AA versus AA → DCT. Models assuming within-patient independence overestimated OS time, which corrected with the calibration approach. With generic pricing, AA → DCT dominated DCT → AA, (higher 5-y QALYs and lower costs), consistent for all values of calibration parameters (including no correction). Model calibration increased the difference in 5-y QALYs between treatment strategies (0.07 uncorrected v. 0.15 with base-case correction). Applying the correction decreased the estimated difference in cost (-$5,360 uncorrected v. -$3,066 corrected). Results were strongly affected by the cost of AA. Under a lifetime horizon, AA → DCT was no longer dominant but still cost-effective (incremental cost-effectiveness ratio: $19,463). We demonstrate a microsimulation approach to study the cost-effectiveness of therapy sequences for advanced prostate cancer, taking care to account for within-patient dependence. We developed a discrete-time state transition model for studying therapy sequence in advanced cancers.Results are sensitive to dependence within patients.A calibration approach can introduce dependence across lines of therapy and closely match simulation outcomes to target trial outcomes.

Effect of sulfuric residual acid on the physicochemical properties, thermal behavior, and decomposition kinetics of nitrocellulose

The acid remaining in the nitrocellulose (NC) during the production process have some influence on its structure and stability. Herein, physicochemical properties, thermal behavior, and decomposition kinetics of NC before and after sulfuric acid treatment were thoroughly investigated using a series of analytical instruments in this paper. It is found that the nitrogen content and relative molecular weight of NC-0.968% that undergoing impregnation with sulfuric acid and drying is decreased by 0.58% and 16% (decreasing rate), respectively, suggesting that the presence of excess sulfuric acid leads to denitrification of NC at room temperature as well as breakage of the glucoside bond, with a greater effect on the latter. Moreover, the results of X-ray diffraction (XRD), scanning electron microscopy (SEM), and dynamic vapor sorption (DVS) indicate that the presence of sulfuric acid did not cause significant changes in the crystalline structure of NC, but severely damage the microscopic morphology, resulting in a large number of micropores appearing on its fiber surface, which enhances the hygroscopicity of NC. In addition, the combined Model-based kinetic results indicate that the decomposition reaction process of NC- 0.968% has a step one more than that of pure NC, which is attributed to the acid-catalyzed denitrification of NC and the breaking of glucoside bonds. Combined with the experimental results of TG-DSC-FITR, it shows that the presence of acid mainly affects the initial decomposition process of NC, while the fracture of the glucose ring frame and oxygen bridge in the NC molecular structure is more dependent on the action of NO2 and the decomposition temperature.

Projected Benefits of Long-Acting Antiretroviral Therapy in Nonsuppressed People With Human Immunodeficiency Virus Experiencing Adherence Barriers.

In a demonstration project, long-acting, injectable cabotegravir-rilpivirine (CAB-RPV) achieved viral suppression in a high proportion of people with HIV (PWH) who were virologically nonsuppressed with adherence barriers. We projected the long-term impact of CAB-RPV for nonsuppressed PWH experiencing adherence barriers. Using the Cost-Effectiveness of Preventing AIDS Complications (CEPAC) model, we compared 3 strategies: (1) standard of care oral integrase inhibitor-based ART (INSTI); (2) INSTI-based ART with supportive social services ("wraparound services" [WS]) (INSTI/WS); and (3) CAB-RPV with WS (CAB-RPV/WS). Model outcomes included viral suppression (%) and engagement in care (%) at 3 years, and life expectancy (life-years [LYs]). Base case cohort characteristics included mean age of 47y (standard deviation [SD], 10y), 90% male at birth, and baseline mean CD4 count 150/µL (SD, 75/µL). Viral suppression at 3 months was 13% (INSTI), 28% (INSTI/WS), and 60% (CAB-RPV/WS). Mean loss to follow-up was 28/100 person-years (PY) (SD, 2/100 PY) without WS and 16/100 PY (SD, 1/100 PY) with WS. Projected viral suppression at 3 years would vary widely: 16% (INSTI), 38% (INSTI/WS), and 44% (CAB-RPV/WS). Life expectancy would be 7.4 LY (INSTI), 9.0 LY (INSTI/WS), and 9.4 LY (CAB-RPV/WS). Projected benefits over oral ART would be greater for PWH initiating CAB-RPV/WS at lower CD4 counts. Across plausible key parameter ranges, CAB-RPV/WS would improve viral suppression and life expectancy compared with oral INSTI strategies. These model-based results support that long-acting injectable CAB-RPV with extensive support services for nonsuppressed PWH experiencing adherence barriers is likely to increase viral suppression and improve survival. A prospective study to provide further evidence is needed.

Excess mortality during the COVID-19 pandemic (2020-2021) in an urban community of Bangladesh.

Measuring COVID-19-related mortality is vital for making public health policy decisions. The magnitude of COVID-19-related mortality is largely unknown in low- and middle-income countries (LMICs), including Bangladesh, due to inadequate COVID-19 testing capacity and a lack of robust civil registration and vital statistics systems. Even with the lack of data, cemetery-based death records in LMICs may provide insightful information on potential COVID-19-related mortality rates; nevertheless, there is a dearth of research employing cemetery-based death records. This study aimed to assess the excess mortality during the COVID-19 pandemic in an urban setting in Bangladesh using a cemetery-based death registration dataset. A total of 6,271 deaths recorded between January 2015 and December 2021 were analysed using a Bayesian structural time series model. Exploratory analysis found that the average monthly number of deaths was 69 during the pre-COVID-19 period (January 2015-February 2020), but significantly increased to 92 during the COVID-19 period (March 2020-December 2021). The increase in male deaths was twice as large as the increase in female deaths. Model-based results were not statistically significant (relative effect 17%, 95% credible interval: -18%, 57%), but there was an overall increasing trend during the COVID-19 period, and specific months or shorter periods had a substantial increase. This first-of-its-kind study in Bangladesh has assessed the excess mortality in an urban community during the COVID-19 pandemic. Cemetery-based death registration appears to aid in tracking population mortality, especially in resource-limited countries where collecting data on the ground is challenging during crisis periods; however, additional large-scale research is required.

Determinants of Household Vulnerability in Networks with Formal Insurance and Informal Risk-Sharing

Effective risk management is key to strengthening the resilience of the most vulnerable. Insurance products can help achieve this goal. Yet, in particular in low-income countries, not all can afford the regular premiums. Instead, informal risk-sharing within social networks plays a crucial role in protecting against unpredictable environmental shocks such as droughts and floods. However, this support may not reach households in need if income is heterogeneously distributed and poor households are not connected to those with sufficient resources to share. To study the determinants of vulnerability to extreme events when insurance is available but not affordable to all, we aggregate outcomes of an empirically informed agent-based model in a regression model. We show that not only a household's own financial situation is important for shock resilience, but also the household's position in the network and the financial situation of connected households. We demonstrate the transferability of our results by achieving high prediction accuracy for an empirical risk-sharing network of a village in the Philippines. The study demonstrates how model-based results can help detect vulnerable households. This can be used to develop a reliable identification tool to leverage external support and effectively target subsidies for different types of shocks.

Association of Gender and Operative Feedback Quality in Surgical Residents

Feedback is critical for learning, however, gender differences exist in the quality of feedback that trainees receive. For example, narrative feedback on surgical trainees' end-of-block rotations differs based on trainee-faculty gender dyads, with female faculty giving higher quality feedback and male trainees receiving higher quality feedback. Though this represents evidence of gender bias in global evaluations, there is limited understanding of how much bias might be present in operative workplace-based assessments (WBAs). In this study, we explore the quality of narrative feedback among trainee-faculty gender dyads in an operative WBA. A previously validated natural language processing model was used to examine instances of narrative feedback and assign a probability of being characterized as high quality feedback (defined as feedback which was relevant as well as corrective and/or specific). A linear mixed model was performed, with probability of high quality feedback as the outcome, and resident gender, faculty gender, PGY, case complexity, autonomy rating, and operative performance rating as explanatory variables. Analyses included 67,434 SIMPL operative performance evaluations (2,319 general surgery residents, 70 institutions) collected from September 2015 through September 2021. Of 36.3% evaluations included narrative feedback. Male faculty were more likely to provide narrative feedback compared to female faculty. Mean probabilities of receiving high quality feedback ranged from 81.6 (female faculty-male resident) to 84.7 (male faculty-female resident). Model-based results demonstrated that female residents were more likely to receive high quality feedback (p < 0.01), however, there was no significant difference in probability of high quality narrative feedback based on faculty-resident gender dyad (p = 0.77). Our study revealed resident gender differences in the probability of receiving high-quality narrative feedback following a general surgery operation. However, we found no significant differences based on faculty-resident gender dyad. Male faculty were more likely to provide narrative feedback compared to their female colleagues. Further research using general surgery resident-specific feedback quality models may be warranted.

Changes of Resection Goal After Using 3-Dimensional Printing Brain Tumor Model for Presurgical Planning

Conventional 2D MRI-based neuronavigation systems can improve the maximal safe resection in brain tumor surgery but can be unintuitive. A 3D-printed brain tumor model allows for a more intuitive and stereoscopic understanding of brain tumors and adjacent neurovascular structures. This study aimed to identify the clinical efficacy of a 3D-printed brain tumor model in presurgical planning by focusing on differences in the extent of resection (EOR). Thirty-two neurosurgeons (14 faculty members, 11 fellows, 7 residents) randomly selected the two 3D-printed brain tumor models from the 10 manufactured models and performed presurgical planning following a standardized questionnaire. To compare the 2D MRI-based planning results with the 3D-printed model-based planning results, we analyzed the changing patterns and characteristics of the EOR. Of 64 randomly generated cases, the resection goal changed in 12 cases (18.8%). When the tumor was located intra-axially, the surgical posture required a prone position, and when the neurosurgeon was dexterous in surgery, there was a higher rate of EOR changes. 3D-printed models 2, 4, and 10, which all represented tumors in the posterior of the brain, had high rates of changing EOR. A 3D-printed brain tumor model could be utilized in presurgical planning to effectively determine the EOR.