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Abstract Lifestyle diseases significantly contribute to the global health burden, with lifestyle factors playing a crucial role in the development of depression. The COVID-19 pandemic has intensified many determinants of depression. This study aimed to identify lifestyle and demographic factors associated with depression symptoms among Indians during the pandemic, focusing on a sample from Kolkata, India. An online public survey was conducted, gathering data from 1,834 participants (with 1,767 retained post-cleaning) over three months via social media and email. The survey consisted of 44 questions and was distributed anonymously to ensure privacy. Data were analyzed using statistical methods and machine learning, with principal component analysis (PCA) and analysis of variance (ANOVA) employed for feature selection. K-means clustering divided the pre-processed dataset into five clusters, and a support vector machine (SVM) with a linear kernel achieved 96% accuracy in a multi-class classification problem. The Local Interpretable Model-agnostic Explanations (LIME) algorithm provided local explanations for the SVM model predictions. Additionally, an OWL (web ontology language) ontology facilitated the semantic representation and reasoning of the survey data. The study highlighted a pipeline for collecting, analyzing, and representing data from online public surveys during the pandemic. The identified factors were correlated with depressive symptoms, illustrating the significant influence of lifestyle and demographic variables on mental health. The online survey method proved advantageous for data collection, visualization, and cost-effectiveness while maintaining anonymity and reducing bias. Challenges included reaching the target population, addressing language barriers, ensuring digital literacy, and mitigating dishonest responses and sampling errors. In conclusion, lifestyle and demographic factors significantly impact depression during the COVID-19 pandemic. The study’s methodology offers valuable insights into addressing mental health challenges through scalable online surveys, aiding in the understanding and mitigation of depression risk factors.

Abstract There is a large body of evidence that cellular metabolism governs inflammation, and that inflammation contributes to the progression of atherosclerosis. However, whether mitochondrial DNA synthesis affects macrophage function and atherosclerosis pathology is not fully understood. Here we show, by transcriptomic analyzes of plaque macrophages, spatial single cell transcriptomics of atherosclerotic plaques, and functional experiments, that mitochondrial DNA (mtDNA) synthesis in atherosclerotic plaque macrophages are triggered by vascular cell adhesion molecule 1 (VCAM-1) under inflammatory conditions in both humans and mice. Mechanistically, VCAM-1 activates C/EBPα, which binds to the promoters of key mitochondrial biogenesis genes - Cmpk2 and Pgc1a. Increased CMPK2 and PGC-1α expression triggers mtDNA synthesis, which activates STING-mediated inflammation. Consistently, atherosclerosis and inflammation are less severe in Apoe −/− mice lacking Vcam1 in macrophages. Downregulation of macrophage-specific VCAM-1 in vivo leads to decreased expression of LYZ1 and FCOR, involved in STING signalling. Finally, VCAM-1 expression in human carotid plaque macrophages correlates with necrotic core area, mitochondrial volume, and oxidative damage to DNA. Collectively, our study highlights the importance of macrophage VCAM-1 in inflammation and atherogenesis pathology and proposes a self-acerbating pathway involving increased mtDNA synthesis.

Only a third of tuberculosis (TB) cases in Nigeria in 2020 were diagnosed and notified, in part due to low detection and under-reporting from the private health sector. Using a standardised patient (SP) survey approach, we assessed how management of presumptive TB in the private sector aligns with national guidelines and whether this differed from a study conducted before the start of the COVID-19 pandemic. 13 SPs presented a presumptive TB case to 511 private providers in urban areas of Lagos and Kano states in May and June 2021. Private provider case management was compared with national guidelines divided into three main steps: SP questioned about cough duration; sputum collection attempted for TB testing; and non-prescription of anti-TB medications, antibiotics and steroids. SP visits conducted in May–June 2021 were directly compared to SP visits conducted in the same areas in June–July 2019. Overall, 28% of interactions (145 of 511, 95% CI 24.5% to 32.5%) were correctly managed according to Nigerian guidelines, as few providers completed all three necessary steps. Providers in 71% of visits asked about cough duration (362 of 511, 95% CI 66.7% to 74.7%), 35% tested or recommended a sputum test (181 of 511, 95% CI 31.3% to 39.8%) and 79% avoided prescribing or dispensing unnecessary medications (406 of 511, 95% CI 75.6% to 82.8%). COVID-19 related questions were asked in only 2.4% (12 of 511, 95% CI 1.3% to 4.2%) of visits. During the COVID-19 pandemic, few providers completed all steps of the national guidelines. Providers performed better on individual steps, particularly asking about symptoms and avoiding prescription of harmful medications. Comparing visits conducted before and during the COVID-19 pandemic showed that COVID-19 did not significantly change the quality of TB care.

New sublineages of SARS-CoV-2 variants-of-concern (VOCs) continuously emerge with mutations in the spike glycoprotein. In most cases, the sublineage-defining mutations vary between the VOCs. It is unclear whether these differences reflect lineage-specific likelihoods for mutations at each spike position or the stochastic nature of their appearance. Here we show that SARS-CoV-2 lineages have distinct evolutionary spaces (a probabilistic definition of the sequence states that can be occupied by expanding virus subpopulations). This space can be accurately inferred from the patterns of amino acid variability at the whole-protein level. Robust networks of co-variable sites identify the highest-likelihood mutations in new VOC sublineages and predict remarkably well the emergence of subvariants with resistance mutations to COVID-19 therapeutics. Our studies reveal the contribution of low frequency variant patterns at heterologous sites across the protein to accurate prediction of the changes at each position of interest.

Summary: Background: Emerging research indicates growing concern over long COVID globally, although there have been limited studies that estimate population burden. We aimed to estimate the burden of long COVID in three districts of Haryana, India, using an opportunity to link a seroprevalence study to follow-up survey of symptoms associated with long COVID. Methods: We used a population-based seroprevalence survey for COVID-19 conducted in September 2021 across Haryana, India. Adults from three purposively selected districts (Rohtak, Gurugram, and Jhajjar) were eligible to participate; 2205 of 3213 consented to participate in a survey on health status. Trained investigators administered a structured questionnaire that included demographic characteristics, self-reported symptoms of illness in the last six months before the survey, mental health, and history of COVID-19. Findings: Unadjusted regression estimates indicated positive correlations between symptomatic complaints and COVID-19 exposure, suggesting lingering effects of COVID-19 in this population. The overall physical morbidity index was higher among those who tested positive for COVID-19, as was the incidence of new cases. However, both morbidity and incidence became statistically insignificant after adjustment for multiple comparisons. Cough emerged as the only statistically significant individual persistent symptom. Sex-stratified analyses indicated significant estimates only for physical morbidity in women. Interpretation: This study is one of the first from India that uses a large population-based sample to examine longer term repercussions of COVID infections. The burden of long COVID should primarily be addressed in clinical settings, where specialised treatment for individual cases continues to evolve. Our analyses also provide insight into the size and nature of studies required to assess the population-level burden of long COVID. Funding: This paper was produced under the auspices of the Lancet COVID 19 Commission India Task Force, which was supported financially by the Reliance Foundation. The Lancet COVID 19 Commission was set up in July 2020 and submitted its final report by October 2022. This report by the India Task Force was prepared during the same period.

Abstract Background Indonesia has the second highest incidence of tuberculosis in the world. While 74% of people with tuberculosis in Indonesia first accessed the private health sector when seeking care for their symptoms, only 18% of tuberculosis notifications originate in the private sector. Little is known about the impact of the COVID-19 pandemic on the private sector. Using unannounced standardized patient visits to private providers, we aimed to measure quality of tuberculosis care during the COVID-19 pandemic. Methods A cross-sectional study was conducted using standardized patients in Bandung City, West Java, Indonesia. Ten standardized patients completed 292 visits with private providers between 9 July 2021 and 21 January 2022, wherein standardized patients presented a presumptive tuberculosis case. Results were compared to standardized patients surveys conducted in the same geographical area before the onset of COVID-19. Results Overall, 35% (95% confidence interval (CI): 29.2–40.4%) of visits were managed correctly according to national tuberculosis guidelines. There were no significant differences in the clinical management of presumptive tuberculosis patients before and during the COVID-19 pandemic, apart from an increase in temperature checks (adjusted odds ratio (aOR): 8.05, 95% CI: 2.96–21.9, p

Abstract Background e-Health has played a crucial role during the COVID-19 pandemic in primary health care. e-Health is the cost-effective and secure use of Information and Communication Technologies (ICTs) to support health and health-related fields. Various stakeholders worldwide use ICTs, including individuals, non-profit organizations, health practitioners, and governments. As a result of the COVID-19 pandemic, ICT has improved the quality of healthcare, the exchange of information, training of healthcare professionals and patients, and facilitated the relationship between patients and healthcare providers. This study systematically reviews the literature on ICT-based automatic and remote monitoring methods, as well as different ICT techniques used in the care of COVID-19-infected patients. Objective The purpose of this systematic literature review is to identify the e-Health methods, associated ICTs, method implementation strategies, information collection techniques, advantages, and disadvantages of remote and automatic patient monitoring and care in COVID-19 pandemic. Methods The search included primary studies that were published between January 2020 and June 2022 in scientific and electronic databases, such as EBSCOhost, Scopus, ACM, Nature, SpringerLink, IEEE Xplore, MEDLINE, Google Scholar, JMIR, Web of Science, Science Direct, and PubMed. In this review, the findings from the included publications are presented and elaborated according to the identified research questions. Evidence-based systematic reviews and meta-analyses were conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. Additionally, we improved the review process using the Rayyan tool and the Scale for the Assessment of Narrative Review Articles (SANRA). Among the eligibility criteria were methodological rigor, conceptual clarity, and useful implementation of ICTs in e-Health for remote and automatic monitoring of COVID-19 patients. Results Our initial search identified 664 potential studies; 102 were assessed for eligibility in the pre-final stage and 65 articles were used in the final review with the inclusion and exclusion criteria. The review identified the following eHealth methods—Telemedicine, Mobile Health (mHealth), and Telehealth. The associated ICTs are Wearable Body Sensors, Artificial Intelligence (AI) algorithms, Internet-of-Things, or Internet-of-Medical-Things (IoT or IoMT), Biometric Monitoring Technologies (BioMeTs), and Bluetooth-enabled (BLE) home health monitoring devices. Spatial or positional data, personal and individual health, and wellness data, including vital signs, symptoms, biomedical images and signals, and lifestyle data are examples of information that is managed by ICTs. Different AI and IoT methods have opened new possibilities for automatic and remote patient monitoring with associated advantages and weaknesses. Our findings were represented in a structured manner using a semantic knowledge graph (e.g., ontology model). Conclusions Various e-Health methods, related remote monitoring technologies, different approaches, information categories, the adoption of ICT tools for an automatic remote patient monitoring (RPM), advantages and limitations of RMTs in the COVID-19 case are discussed in this review. The use of e-Health during the COVID-19 pandemic illustrates the constraints and possibilities of using ICTs. ICTs are not merely an external tool to achieve definite remote and automatic health monitoring goals; instead, they are embedded in contexts. Therefore, the importance of the mutual design process between ICT and society during the global health crisis has been observed from a social informatics perspective. A global health crisis can be observed as an information crisis (e.g., insufficient information, unreliable information, and inaccessible information); however, this review shows the influence of ICTs on COVID-19 patients' health monitoring and related information collection techniques.

The COVID-19 pandemic is thought to have undone years' worth of progress in the fight against tuberculosis (TB). For instance, in Indonesia, a high TB burden country, TB case notifications decreased by 14% and treatment coverage decreased by 47% during COVID-19. We sought to better understand the impact of COVID-19 on TB case detection using two cross-sectional surveys conducted before (2018) and after the onset of the pandemic (2021). These surveys allowed us to quantify the delays that individuals with TB who eventually received treatment at private providers faced while trying to access care for their illness, their journey to obtain a diagnosis, the encounters individuals had with healthcare providers before a TB diagnosis, and the factors associated with patient delay and the total number of provider encounters. We found some worsening of care seeking pathways on multiple dimensions. Median patient delay increased from 28 days (IQR: 10, 31) to 32 days (IQR: 14, 90) and the median number of encounters increased from 5 (IQR: 4, 8) to 7 (IQR: 5, 10), but doctor and treatment delays remained relatively unchanged. Employed individuals experienced shorter delays compared to unemployed individuals (adjusted medians: -20.13, CI -39.14, -1.12) while individuals whose initial consult was in the private hospitals experienced less encounters compared to those visiting public providers, private primary care providers, and informal providers (-4.29 encounters, CI -6.76, -1.81). Patients who visited the healthcare providers >6 times experienced longer total delay compared to those with less than 6 visits (adjusted medians: 59.40, 95% CI: 35.04, 83.77). Our findings suggest the need to ramp up awareness programs to reduce patient delay and strengthen private provide engagement in the country, particularly in the primary care sector.

ABSTRACTA significant surge in research endeavors leverages the vast potential of high-throughput omic technology platforms for broad profiling of biological responses to vaccines and cutting-edge immunotherapies and stem-cell therapies under development. These profiles capture different aspects of core regulatory and functional processes at different scales of resolution from molecular and cellular to organismal. Systems approaches capture the complex and intricate interplay between these layers and scales. Here, we summarize experimental data modalities, for characterizing the genome, epigenome, transcriptome, proteome, metabolome, and antibody-ome, that enable us to generate large-scale immune profiles. We also discuss machine learning and network approaches that are commonly used to analyze and integrate these modalities, to gain insights into correlates and mechanisms of natural and vaccine-mediated immunity as well as therapy-induced immunomodulation.

Alterations in lipid metabolism and circulating lipid species have been reported in patients with acute critical illness. Here the authors show that selective rise in systemic phosphatidylethanolamine levels is a common feature of critical illness that associates with worse clinical outcomes.

Summary: The 2013 Ebola epidemic in Central and West Africa heralded the emergence of wide-spread, highly pathogenic viruses. The successful recombinant vector vaccine against Ebola (rVSVΔG-ZEBOV-GP) will limit future outbreaks, but identifying mechanisms of protection is essential to protect the most vulnerable. Vaccine-induced antibodies are key determinants of vaccine efficacy, yet the mechanism by which vaccine-induced antibodies prevent Ebola infection remains elusive. Here, we exploit a break in long-term vaccine efficacy in non-human primates to identify predictors of protection. Using unbiased humoral profiling that captures neutralization and Fc-mediated functions, we find that antibodies specific for soluble glycoprotein (sGP) drive neutrophil-mediated phagocytosis and predict vaccine-mediated protection. Similarly, we show that protective sGP-specific monoclonal antibodies have elevated neutrophil-mediated phagocytic activity compared with non-protective antibodies, highlighting the importance of sGP in vaccine protection and monoclonal antibody therapeutics against Ebola virus.

As the first point of care for many healthcare seekers, private pharmacies play an important role in tuberculosis (TB) care. However, previous studies in India have showed that private pharmacies commonly dispense symptomatic treatments and broad-spectrum antibiotics over-the-counter (OTC), rather than referring patients for TB testing. Such inappropriate management by pharmacies can delaye TB diagnosis. We assessed medical advice and OTC drug dispensing practices of pharmacists for standardized patients presenting with classic symptoms of pulmonary TB (case 1) and for those with sputum smear positive pulmonary TB (case 2), and examined how practices have changed over time in an urban Indian site. We examined how and whether private pharmacies improved practices for TB in 2019 compared to a baseline study conducted in 2015 in the city of Patna, using the same survey sampling techniques and study staff. The proportion of patient-pharmacist interactions that resulted in correct or ideal management, as well as the proportion of interactions resulting in antibiotic, quinolone, and corticosteroid are presented, with standard errors clustered at the provider level. To assess the difference in case management and the use of drugs across the two cases by round, a difference in difference (DiD) model was employed. A total of 936 SP interactions were completed over both rounds of survey. Our results indicate that across both rounds of data collection, 331 of 936 (35%; 95% CI: 32-38%) of interactions were correctly managed. At baseline, 215 of 500 (43%; 95% CI: 39-47%) of interactions were correctly managed whereas 116 of 436 (27%; 95% CI: 23-31%) were correctly managed in the second round of data collection. Ideal management, where in addition to a referral, patients were not prescribed any potentially harmful medications, was seen in 275 of 936 (29%; 95% CI: 27-32%) of interactions overall, with 194 of 500 (39%; 95% CI: 35-43%) of interactions at baseline and 81 of 436 (19%; 95% CI: 15-22%) in round 2. No private pharmacy dispensed anti-TB medications without a prescription. On average, the difference in correct case management between case 1 vs. case 2 dropped by 20 percent points from baseline to the second round of data collection. Similarly, ideal case management decreased by 26 percentage points between rounds. This is in contrast with the dispensation of medicines, which had the opposite effect between rounds; the difference in dispensation of quinolones between case 1 and case 2 increased by 14 percentage points, as did corticosteroids by 9 percentage points, antibiotics by 25 percentage points and medicines generally by 30 percentage points. Our standardised patient study provides valuable insights into how private pharmacies in an Indian city changed their management of patients with TB symptoms or with confirmed TB over a 5-year period. We saw that overall, private pharmacy performance has weakened over time. However, no OTC dispensation of anti-TB medications occurred in either survey round. As the first point of contact for many care seekers, continued and sustained efforts to engage with Indian private pharmacies should be prioritized.

Nigeria has the second largest share of undiagnosed TB cases in the world and a large private health sector estimated to be the point of initial care-seeking for 67% of TB patients. There is evidence that COVID-19 restrictions disrupted private healthcare provision, but insufficient data on how private healthcare provision changed as a result of the pandemic. We conducted qualitative interviews and a survey to assess the impact of the pandemic, and government response on private healthcare provision, and the disruptions providers experienced, particularly for TB services. Using mixed methods, we targeted policymakers, and a network of clinical facilities, laboratories, community pharmacies, and medicine vendors in Kano and Lagos, Nigeria. We interviewed 11 policymakers, surveyed participants in 2,412 private facilities. Most (n = 1,676, 70%) facilities remained open during the initial lockdown period, and most (n = 1,667, 69%) offered TB screening. TB notifications dipped during the lockdown periods but quickly recovered. Clinical facilities reported disruptions in availability of medical supplies, staff, required renovations, patient volume and income. Few private providers (n = 119, 11% in Kano; n = 323, 25% in Lagos) offered any COVID-19 screening up to the time of the survey, as these were only available in designated facilities. These findings aligned with the interviews as policymakers reported a gradual return to pre-COVID services after initial disruptions and diversion of resources to the pandemic response. Our results show that COVID-19 and control measures had a temporary impact on private sector TB care. Although some facilities saw decreases in TB notifications, private facilities continued to provide care for individuals with TB who otherwise might have been unable to seek care in the public sector. Our findings highlight resilience in the private sector as they recovered fairly quickly from pandemic-related disruptions, and the important role private providers can play in supporting TB control efforts.

Background There are few rigorous studies comparing quality of tuberculosis (TB) care in public versus private sectors.Methods We used standardised patients (SPs) to measure technical quality and patient experience in a sample of private and public facilities in Mumbai.Results SPs presented a ‘classic, suspected TB’ scenario and a ‘recurrence or drug-resistance’ scenario. In the private sector, SPs completed 643 interactions. In the public sector, 164 interactions. Outcomes included indicators of correct management, medication use and client experience. Public providers used microbiological testing (typically, microscopy) more frequently, in 123 of 164 (75%; 95% CI 68% to 81%) vs 223 of 644 interactions (35%; 95% CI 31% to 38%) in the private sector. Private providers were more likely to order chest X-rays, in 556 of 639 interactions (86%; 95% CI 84% to 89%). According to national TB guidelines, we found higher proportions of correct management in the public sector (75% vs 35%; (adjusted) difference 35 percentage points (pp); 95% CI 25 to 46). If X-rays were considered acceptable for the first case but drug-susceptibility testing was required for the second case, the private sector correctly managed a slightly higher proportion of interactions (67% vs 51%; adjusted difference 16 pp; 95% CI 7 to 25). Broad-spectrum antibiotics were used in 76% (95% CI 66% to 84%) of the interactions in public hospitals, and 61% (95% CI 58% to 65%) in private facilities. Costs in the private clinics averaged rupees INR 512 (95% CI 485 to 539); public facilities charged INR 10. Private providers spent more time with patients (4.4 min vs 2.4 min; adjusted difference 2.0 min; 95% CI 1.2 to 2.9) and asked a greater share of relevant questions (29% vs 43%; adjusted difference 13.7 pp; 95% CI 8.2 to 19.3).Conclusions While the public providers did a better job of adhering to national TB guidelines (especially microbiological testing) and offered less expensive care, private sector providers did better on client experience.

Background: India’s dominant private healthcare sector is the destination for 60–85% of initial tuberculosis care-seeking. The COVID-19 pandemic in India drastically affected TB case notifications in the first half of 2020. In this survey, we assessed the impact of the first wave of COVID-19 in India on private providers, and changes they adopted in their practice due to the pandemic. Methods: The Joint Effort for Elimination of TB (JEET) is a nationwide Global Fund project implemented across 406 districts in 23 states to extend quality TB services to patients seeking care in private sector. We conducted a rapid survey of 11% (2,750) of active providers engaged under JEET’s intense Patient Provider Support Agency (PPSA) model across 15 Indian states in Q1 (February–March) of 2021. Providers were contacted in person or telephonically, and consenting participants were interviewed using a web-based survey tool. Responses from participants were elicited on their practice before COVID-19, during the 2020 lockdowns (March–April 2020) and currently (Q1 2021). Data were adjusted for survey design and non-response, and results were summarised using descriptive statistics and logistic regression. Results: Of the 2,750 providers sampled, 2,011 consented and were surveyed (73 % response). Nearly 50 % were between 30 and 45 years of age, and 51 % were from Uttar Pradesh, Maharashtra and Gujarat. Seventy percent of providers reported reduced daily out-patient numbers in Q1 2021 compared to pre-COVID times. During the lockdown, 898 (40 %) of providers said their facilities were closed, while 323 (11 %) offered limited services including teleconsultation. In Q1 2021, 88 % of provider facilities were fully open, with 10 % providing adjusted services, and 4 % using teleconsultation. Only 2 % remained completely closed. Majority of the providers (92 %) reported not experiencing any delays in TB testing in Q1 2021 compared to pre-COVID times. Only 6 % reported raising costs at their clinic, mostly to cover personal protective equipment (PPE) and other infection control measures, although 60–90 % implemented various infection control measures. Thirty-three percent of TB providers were ordering COVID-19 testing, in addition to TB testing.To adapt, 82% of survey providers implemented social distancing and increased timing between appointments and 83% started conducting temperature checks, with variation by state and provider type, while 89% adopted additional sanitation measures in their facilities. Furthermore, 62% of providers started using PPE, and 13% made physical changes (air filters, isolation of patient areas) to their clinic to prevent infection. Seventy percent of providers stated that infection control measures could decrease TB transmission. Conclusion: Although COVID-19 restrictions resulted in significant declines in patient turn-out at private facilities, our analysis showed that most providers were open and costs for TB care remained mostly the same in Q1 2021. As result of the COVID-19 pandemic, several positive strategies have been adapted by the private sector TB care providers. Since the subsequent COVID-19 waves were more severe or widespread, additional work is needed to assess the impact of the pandemic on the private health sector.

Severe COVID-19 disease is associated with dysregulation of the myeloid compartment during acute infection. Survivors frequently experience long-lasting sequelae, but little is known about the eventual persistence of this immune alteration. Herein, we evaluated TLR-induced cytokine responses in a cohort of mild to critical patients during acute or convalescent phases (n = 97). In the acute phase, we observed impaired cytokine production by monocytes in the patients with the most severe COVID-19. This capacity was globally restored in convalescent patients. However, we observed increased responsiveness to TLR1/2 ligation in patients who recovered from severe disease, indicating that these cells display distinct functional properties at the different stages of the disease. In patients with acute severe COVID-19, we identified a specific transcriptomic and epigenomic state in monocytes that can account for their functional refractoriness. The molecular profile of monocytes from recovering patients was distinct and characterized by increased chromatin accessibility at activating protein 1 (AP1) and MAF loci. These results demonstrate that severe COVID-19 infection has a profound impact on the differentiation status and function of circulating monocytes, during both the acute and the convalescent phases, in a completely distinct manner. This could have important implications for our understanding of short- and long-term COVID-19–related morbidity.

Summary: High-dimensional cellular and molecular profiling of biological samples highlights the need for analytical approaches that can integrate multi-omic datasets to generate prioritized causal inferences. Current methods are limited by high dimensionality of the combined datasets, the differences in their data distributions, and their integration to infer causal relationships. Here, we present Essential Regression (ER), a novel latent-factor-regression-based interpretable machine-learning approach that addresses these problems by identifying latent factors and their likely cause-effect relationships with system-wide outcomes/properties of interest. ER can integrate many multi-omic datasets without structural or distributional assumptions regarding the data. It outperforms a range of state-of-the-art methods in terms of prediction. ER can be coupled with probabilistic graphical modeling, thereby strengthening the causal inferences. The utility of ER is demonstrated using multi-omic system immunology datasets to generate and validate novel cellular and molecular inferences in a wide range of contexts including immunosenescence and immune dysregulation. The bigger picture: Multi-omic technologies for deep cellular and molecular profiling from model organisms or humans have rapidly expanded. However, existing analytical approaches are constrained by the high dimensionality of these datasets, differences in data distributions, and the inability to generate causal inference beyond predictive biomarkers. To address these issues, we developed a novel interpretable machine-learning framework, Essential Regression (ER). ER integrates high-dimensional multi-omic datasets without distributional assumptions regarding the data and identifies significant latent factors and their causal relationships with system-wide outcomes/properties of interest. ER uses higher-order relationships encapsulated in the latent factors, rather than the individual observables, to home in on novel mechanistic insights. Our approach outperforms a range of state-of-the-art methods in terms of prediction and generates novel immunological inferences, consistent with evidence in model organisms.

The novel SARS-CoV-2 virus, as it manifested in India in April 2020, showed marked heterogeneity in its transmission. Here, we used data collected from contact tracing during the lockdown in response to the first wave of COVID-19 in Punjab, a major state in India, to quantify this heterogeneity, and to examine implications for transmission dynamics. We found evidence of heterogeneity acting at multiple levels: in the number of potentially infectious contacts per index case, and in the per-contact risk of infection. Incorporating these findings in simple mathematical models of disease transmission reveals that these heterogeneities act in combination to strongly influence transmission dynamics. Standard approaches, such as representing heterogeneity through secondary case distributions, could be biased by neglecting these underlying interactions between heterogeneities. We discuss implications for policy, and for more efficient contact tracing in resource-constrained settings such as India. Our results highlight how contact tracing, an important public health measure, can also provide important insights into epidemic spread and control.

BackgroundCiliary defects cause heterogenous phenotypes related to mutation burden which lead to impaired development. A previously reported homozygous deletion in the Man1a2 gene causes lethal respiratory failure in newborn pups and decreased lung ciliation compared with wild type (WT) pups. The effects of heterozygous mutation, and the potential for rescue are not known.PurposeWe hypothesized that survival and lung ciliation, (a) would decrease progressively in Man1a2+/− heterozygous and Man1a2–/– null newborn pups compared with WT, and (b) could be enhanced by gestational treatment with N-Acetyl-cysteine (NAC), an antioxidant.MethodsMan1a2+/– adult mice were fed NAC or placebo from a week before breeding through gestation. Survival of newborn pups was monitored for 24 h. Lungs, liver and tails were harvested for morphology, genotyping, and transcriptional profiling.ResultsSurvival (p = 0.0001, Kaplan-Meier) and percent lung ciliation (p = 0.0001, ANOVA) measured by frequency of Arl13b+ respiratory epithelial cells decreased progressively, as hypothesized. Compared with placebo, gestational NAC treatment enhanced (a) lung ciliation in pups with each genotype, (b) survival in heterozygous pups (p = 0.017) but not in WT or null pups. Whole transcriptome of lung but not liver demonstrated patterns of up- and down-regulated genes that were identical in living heterozygous and WT pups, and completely opposite to those in dead heterozygous and null pups. Systems biology analysis enabled reconstruction of protein interaction networks that yielded functionally relevant modules and their interactions. In these networks, the mutant Man1a2 enzyme contributes to abnormal synthesis of proteins essential for lung development. The associated unfolded protein, hypoxic and oxidative stress responses can be mitigated with NAC. Comparisons with the developing human fetal lung transcriptome show that NAC likely restores normal vascular and epithelial tube morphogenesis in Man1a2 mutant mice.ConclusionSurvival and lung ciliation in the Man1a2 mutant mouse, and its improvement with N-Acetyl cysteine is genotype-dependent. NAC-mediated rescue depends on the central role for oxidative and hypoxic stress in regulating ciliary function and organogenesis during development.

Low frequency coding single-nucleotide variants (SNVs) are predicted to disproportionately affect protein function. Here, the authors evaluate 2,009 missense SNVs across 2,185 protein-protein interactions using yeast two-hybrid and protein complementation assays and find that disruptive SNVs often occur in disease-associated genes.

Summary: LOVE, a robust, scalable latent model-based clustering method for biological discovery, can be used across a range of datasets to generate both overlapping and non-overlapping clusters. In our formulation, a cluster comprises variables associated with the same latent factor and is determined from an allocation matrix that indexes our latent model. We prove that the allocation matrix and corresponding clusters are uniquely defined. We apply LOVE to biological datasets (gene expression, serological responses measured from HIV controllers and chronic progressors, vaccine-induced humoral immune responses) resulting in meaningful biological output. For all three datasets, the clusters generated by LOVE remain stable across tuning parameters. Finally, we compared LOVE's performance to that of 13 state-of-the-art methods using previously established benchmarks and found that LOVE outperformed these methods across datasets. Our results demonstrate that LOVE can be broadly used across large-scale biological datasets to generate accurate and meaningful overlapping and non-overlapping clusters. : Biological Sciences, Bioinformatics, Statistical Computing Subject Areas: Biological Sciences, Bioinformatics, Statistical Computing

Admission-based circulating biomarkers for the prediction of outcomes in trauma patients could be useful for clinical decision support. It is unknown which molecular classes of biomolecules can contribute biomarkers to predictive modeling. Here, we analyzed a large multi-omic database of over 8500 markers (proteomics, metabolomics, and lipidomics) to identify prognostic biomarkers in the circulating compartment for adverse outcomes, including mortality and slow recovery, in severely injured trauma patients. Admission plasma samples from patients (n = 129) enrolled in the Prehospital Air Medical Plasma (PAMPer) trial were analyzed using mass spectrometry (metabolomics and lipidomics) and aptamer-based (proteomics) assays. Biomarkers were selected via Least Absolute Shrinkage and Selection Operator (LASSO) regression modeling and machine learning analysis. A combination of five proteins from the proteomic layer was best at discriminating resolvers from non-resolvers from critical illness with an Area Under the Receiver Operating Characteristic curve (AUC) of 0.74, while 26 multi-omic features predicted 30-day survival with an AUC of 0.77. Patients with traumatic brain injury as part of their injury complex had a unique subset of features that predicted 30-day survival. Our findings indicate that multi-omic analyses can identify novel admission-based prognostic biomarkers for outcomes in trauma patients. Unique biomarker discovery also has the potential to provide biologic insights.

While antiretroviral therapy (ART) has effectively revolutionized HIV care, the virus is never fully eliminated. Instead, immune dysfunction, driven by persistent non-specific immune activation, ensues and progressively leads to premature immunologic aging. Current biomarkers monitoring immunologic changes encompass generic inflammatory biomarkers, that may also change with other infections or disease states, precluding the antigen-specific monitoring of HIV-infection associated changes in disease. Given our growing appreciation of the significant changes in qualitative and quantitative properties of disease-specific antibodies in HIV infection, we used a systems approach to explore humoral profiles associated with HIV control. We found that HIV-specific antibody profiles diverge by spontaneous control of HIV, treatment status, viral load and reservoir size. Specifically, HIV-specific antibody profiles representative of changes in viral load were largely quantitative, reflected by differential HIV-specific antibody levels and Fc-receptor binding. Conversely, HIV-specific antibody features that tracked with reservoir size exhibited a combination of quantitative and qualitative changes marked by more distinct subclass selection profiles and unique HIV-specific Fc-glycans. Our analyses suggest that HIV-specific antibody Fc-profiles provide antigen-specific resolution on both cell free and cell-associated viral loads, pointing to potentially novel biomarkers to monitor reservoir activity.

Introduction Determining whether antibiotic prescriptions are inappropriate requires knowledge of patients’ underlying conditions. In low-income and middle-income countries (LMICs), where misdiagnoses are frequent, this is challenging. Additionally, such details are often unavailable for prescription audits. Recent studies using standardised patients (SPs) offer a unique opportunity to generate unbiased prevalence estimates of antibiotic overuse, as the research design involves patients with predefined conditions.Methods Secondary analyses of data from nine SP studies were performed to estimate the proportion of SP–provider interactions resulting in inappropriate antibiotic prescribing across primary care settings in three LMICs (China, India and Kenya). In all studies, SPs portrayed conditions for which antibiotics are unnecessary (watery diarrhoea, presumptive tuberculosis (TB), angina and asthma). We conducted descriptive analyses reporting overall prevalence of antibiotic overprescribing by healthcare sector, location, provider qualification and case. The WHO Access–Watch–Reserve framework was used to categorise antibiotics based on their potential for selecting resistance. As richer data were available from India, we examined factors associated with antibiotic overuse in that country through hierarchical Poisson models.Results Across health facilities, antibiotics were given inappropriately in 2392/4798 (49.9%, 95% CI 40.8% to 54.5%) interactions in India, 83/166 (50.0%, 95% CI 42.2% to 57.8%) in Kenya and 259/899 (28.8%, 95% CI 17.8% to 50.8%) in China. Prevalence ratios of antibiotic overuse in India were significantly lower in urban versus rural areas (adjusted prevalence ratio (aPR) 0.70, 95% CI 0.52 to 0.96) and higher for qualified versus non-qualified providers (aPR 1.55, 95% CI 1.42 to 1.70), and for presumptive TB cases versus other conditions (aPR 1.19, 95% CI 1.07 to 1.33). Access antibiotics were predominantly used in Kenya (85%), but Watch antibiotics (mainly quinolones and cephalosporins) were highly prescribed in India (47.6%) and China (32.9%).Conclusion Good-quality SP data indicate alarmingly high levels of antibiotic overprescription for key conditions across primary care settings in India, China and Kenya, with broad-spectrum agents being excessively used in India and China.

Summary: Legionella pneumophila is an intracellular pathogen that requires nutrients from the host for its replication. It has been shown that replicating L. pneumophila prefers amino acids as main sources of carbon and energy. The homeostasis of amino acids in eukaryotic cells is regulated by the transcription factor EB (TFEB), which translocates into the nucleus and activates genes for autophagy and lysosomal biogenesis. Here we show that the Legionella effector SetA causes a robust nuclear translocation of TFEB when exogenously expressed in mammalian cells and that the translocation is dependent on the glucosyltransferase activity of SetA. We further show that SetA directly glucosylates TFEB at multiple sites. Our findings of TFEB glucosylation by SetA may suggest an alternative strategy for exploiting host nutrients in addition to the control of host mTORC1 signaling by L. pneumophila. Our results provide further insight into the molecular mechanism of the delicate TFEB nuclear shuttling.

BackgroundThe widespread use of antibiotics plays a major role in the development and spread of antimicrobial resistance. However, important knowledge gaps still exist regarding the extent of their use in low- and middle-income countries (LMICs), particularly at the primary care level. We performed a systematic review and meta-analysis of studies conducted in primary care in LMICs to estimate the prevalence of antibiotic prescriptions as well as the proportion of such prescriptions that are inappropriate.Methods and findingsWe searched PubMed, Embase, Global Health, and CENTRAL for articles published between 1 January 2010 and 4 April 2019 without language restrictions. We subsequently updated our search on PubMed only to capture publications up to 11 March 2020. Studies conducted in LMICs (defined as per the World Bank criteria) reporting data on medicine use in primary care were included. Three reviewers independently screened citations by title and abstract, whereas the full-text evaluation of all selected records was performed by 2 reviewers, who also conducted data extraction and quality assessment. A modified version of a tool developed by Hoy and colleagues was utilized to evaluate the risk of bias of each included study. Meta-analyses using random-effects models were performed to identify the proportion of patients receiving antibiotics. The WHO Access, Watch, and Reserve (AWaRe) framework was used to classify prescribed antibiotics. We identified 48 studies from 27 LMICs, mostly conducted in the public sector and in urban areas, and predominantly based on medical records abstraction and/or drug prescription audits. The pooled prevalence proportion of antibiotic prescribing was 52% (95% CI: 51%-53%), with a prediction interval of 44%-60%. Individual studies' estimates were consistent across settings. Only 9 studies assessed rationality, and the proportion of inappropriate prescription among patients with various conditions ranged from 8% to 100%. Among 16 studies in 15 countries that reported details on prescribed antibiotics, Access-group antibiotics accounted for more than 60% of the total in 12 countries. The interpretation of pooled estimates is limited by the considerable between-study heterogeneity. Also, most of the available studies suffer from methodological issues and report insufficient details to assess appropriateness of prescription.ConclusionsAntibiotics are highly prescribed in primary care across LMICs. Although a subset of studies reported a high proportion of inappropriate use, the true extent could not be assessed due to methodological limitations. Yet, our findings highlight the need for urgent action to improve prescription practices, starting from the integration of WHO treatment recommendations and the AWaRe classification into national guidelines.Trial registrationPROSPERO registration number: CRD42019123269.

Latency reversal agents (LRA) variably induce HIV re-expression in CD4 T cells but reservoirs are not cleared. Whether HIV epitope presentation is similar between latency reversal and initial infection of CD4 T cells is unknown yet crucial to define immune responses able to detect HIV-infected CD4 T cells after latency reversal. HIV peptides displayed by MHC comes from the intracellular degradation of proteins by proteasomes and post-proteasomal peptidases but the impact of LRAs on antigen processing is not known. Here we show that HDAC inhibitors (HDCAi) reduced cytosolic proteolytic activities while PKC agonists (PKCa) increased them to a lesser extent than that induced by TCR activation. During the cytosolic degradation of long HIV peptides in LRA-treated CD4 T cells extracts, HDACi and PKCa modulated degradation patterns of peptides and altered the production of HIV epitopes in often opposite ways. Beyond known HIV epitopes, HDACi narrowed the coverage of HIV antigenic fragments by 8-11aa degradation peptides while PKCa broadened it. LRAs altered HIV infection kinetics and modulated CD8 T cell activation in an epitope- and time-dependent manner. Interestingly the efficiency of endogenous epitope processing and presentation to CD8 T cells was increased by PKCa Ingenol at early time points despite low levels of antigens. LRA-induced modulations of antigen processing should be considered and exploited to enhance and broaden HIV peptide presentation by CD4 T cells and to improve immune recognition after latency reversal. This property of LRAs, if confirmed with other antigens, might be exploited to improve immune detection of diseased cells beyond HIV.

Folic acid plays an essential role in the central nervous system and cancer. This study aimed to screen genes related to folic acid metabolism. Datasets (GSE80587, GSE65267 and GSE116299) correlated to folic acid were screened in the Gene Expression Omnibus. Weighed gene co-expression network analysis was performed to identify modules associated with sample traits of folic acid and organs (brain, prostate and kidney). Functional enrichment analysis was performed for the eigengenes in modules that were significantly correlated with sample traits. Accordingly, the hub genes and key nodes in the modules were identified using the protein interaction network. A total of 17,252 genes in three datasets were identified. One module, which included 97 genes that were highly correlated with sample traits (including folic acid treatment [cor = -0.57, P = 3e-04] and kidney [cor = -0.68, p = 4e-06]), was screened out. Hub genes, including tetratricopeptide repeat protein 38 (Ttc38) and miR-185, as well as those (including Sema3A, Insl3, Dll1, Msh4 and Snai1) associated with “neuropilin binding”, “regulation of reproductive process” and “vitamin D metabolic process”, were identified. Genes, including Ttc38, Sema3A, Insl3, Dll1, Msh4 and Snai1, were the novel factors that may be associated with the development of the kidneys and related to folic acid treatment.

Abstract Systematic reviews are powerful tools for summarizing vast amounts of data in controversial areas; but their utility is limited by methodological choices and assumptions. Two systematic reviews of literature on the quality of private sector primary care in low and middle income countries (LMIC), published in the same journal within a year, reached conflicting conclusions. The difference in findings reflects different review methodologies, but more importantly, a weak underlying body of literature. A detailed examination of the literature cited in both reviews shows that only one of the underlying studies met the gold standard for methodological robustness. Given the current policy momentum on universal health coverage and primary health care reform across the globe, there is an urgent need for high quality empirical evidence on the quality of private versus public sector primary health care in LMIC.

For many patients in India, pharmacies are their first point of contact, where most drugs, including antibiotics, can be purchased over-the-counter (OTC). Recent standardised (simulated) patient studies, covering four Indian cities, provide new insights on how Indian pharmacies manage patients with suspected or known tuberculosis. Correct management of the simulated patients ranged from 13% to 62%, increasing with the certainty of the TB diagnosis. Antibiotics were frequently dispensed OTC to patients, with 16% to 37% receiving such drugs across the cases. On a positive note, these studies showed that no pharmacy dispensed first-line anti-TB drugs. Engagement of pharmacies is important to not only improve TB detection and care, but also limit the abuse of antibiotics.

The use of standardised patients (SPs)—people recruited from the local community to present the same case to multiple providers in a blinded fashion—is increasingly used to measure the quality of care in low-income and middle-income countries. Encouraged by the growing interest in the SP method, and based on our experience of conducting SP studies, we present a conceptual framework for research designs and surveys that use this methodology. We accompany the conceptual framework with specific examples, drawn from our experience with SP studies in low-income and middle-income contexts, including China, India, Kenya and South Africa, to highlight the versatility of the method and illustrate the ongoing challenges. A toolkit and manual for implementing SP studies is included as a companion piece in the online supplement.

Standardized patients (SPs) are people who are recruited locally, trained to make identical scripted clinical presentations, deployed incognito to multiple different health care providers, and debriefed using a structured reporting instrument. The use of SPs has increased dramatically as a method for assessing quality of TB care since it was first validated and used for tuberculosis in 2015. This paper summarizes common findings using 3,086 SP-provider interactions involving tuberculosis across various sampling strata in published studies from India, China, South Africa and Kenya. It then discusses the lessons learned from implementing standardized patients in these diverse settings. First, quality is low: relatively few SPs presenting to a health care provider for the first time were given an appropriate diagnostic test, and most were given unnecessary or inappropriate medication. Second, care takes a wide variety of forms – SPs did not generally receive “wait and see” or “symptomatic” care from providers, but they received a medley of care patterns that included broad-spectrum antibiotics as well as contraindicated quinolone antibiotics and steroids. Third, there is a wide range of estimated quality in each observed sampling stratum: more-qualified providers and higher-level facilities performed better than others in all settings, but in every stratum there were both high- and low-quality providers. Evidence from SP studies paired with medical vignettes has shown that providers of all knowledge levels significantly underperform their demonstrated ability with real patients. Finally, providers showed little response to differences in patient identity, but showed strong responses to differences in case presentation that give some clues as to the reasons for these behaviors. Keywords: Quality of care, Tuberculosis, Standardized patients, Health care providers, Low- and middle-income countries

Summary: Background: In India, men are more likely than women to have active tuberculosis but are less likely to be diagnosed and notified to national tuberculosis programmes. We used data from standardised patient visits to assess whether these gender differences occur because of provider practice. Methods: We sent standardised patients (people recruited from local populations and trained to portray a scripted medical condition to health-care providers) to present four tuberculosis case scenarios to private health-care providers in the cities of Mumbai and Patna. Sampling and weighting allowed for city representative interpretation. Because standardised patients were assigned to providers by a field team blinded to this study, we did balance and placebo regression tests to confirm standardised patients were assigned by gender as good as randomly. Then, by use of linear and logistic regression, we assessed correct case management, our primary outcome, and other dimensions of care by standardised patient gender. Findings: Between Nov 21, 2014, and Aug 21, 2015, 2602 clinical interactions at 1203 private facilities were completed by 24 standardised patients (16 men, eight women). We found standardised patients were assigned to providers as good as randomly. We found no differences in correct management by patient gender (odds ratio 1·05; 95% CI 0·76–1·45; p=0·77) and no differences across gender within any case scenario, setting, provider gender, or provider qualification. Interpretation: Systematic differences in quality of care are unlikely to be a cause of the observed under-representation of men in tuberculosis notifications in the private sector in urban India. Funding: Grand Challenges Canada, Bill & Melinda Gates Foundation, World Bank Knowledge for Change Program.

BackgroundIndia has the highest burden of tuberculosis (TB). Although most patients with TB in India seek care from the private sector, there is limited evidence on quality of TB care or its correlates. Following our validation study on the standardized patient (SP) method for TB, we utilized SPs to examine quality of adult TB care among health providers with different qualifications in 2 Indian cities.Methods and findingsDuring 2014-2017, pilot programs engaged the private health sector to improve TB management in Mumbai and Patna. Nested within these projects, to obtain representative, baseline measures of quality of TB care at the city level, we recruited 24 adults to be SPs. They were trained to portray 4 TB "case scenarios" representing various stages of disease and diagnostic progression. Between November 2014 and August 2015, the SPs visited representatively sampled private providers stratified by qualification: (1) allopathic providers with Bachelor of Medicine, Bachelor of Surgery (MBBS) degrees or higher and (2) non-MBBS providers with alternative medicine, minimal, or no qualifications. Our main outcome was case-specific correct management benchmarked against the Standards for TB Care in India (STCI). Using ANOVA, we assessed variation in correct management and quality outcomes across (a) cities, (b) qualifications, and (c) case scenarios. Additionally, 2 micro-experiments identified sources of variation: first, quality in the presence of diagnostic test results certainty and second, provider consistency for different patients presenting the same case. A total of 2,652 SP-provider interactions across 1,203 health facilities were analyzed. Based on our sampling strategy and after removing 50 micro-experiment interactions, 2,602 interactions were weighted for city-representative interpretation. After weighting, the 473 Patna providers receiving SPs represent 3,179 eligible providers in Patna; in Mumbai, the 730 providers represent 7,115 eligible providers. Correct management was observed in 959 out of 2,602 interactions (37%; 35% weighted; 95% CI 32%-37%), primarily from referrals and ordering chest X-rays (CXRs). Unnecessary medicines were given to nearly all SPs, and antibiotic use was common. Anti-TB drugs were prescribed in 118 interactions (4.5%; 5% weighted), of which 45 were given in the case in which such treatment is considered correct management. MBBS and more qualified providers had higher odds of correctly managing cases than non-MBBS providers (odds ratio [OR] 2.80; 95% CI 2.05-3.82; p < 0.0001). Mumbai non-MBBS providers had higher odds of correct management than non-MBBS in Patna (OR 1.79; 95% CI 1.06-3.03), and MBBS providers' quality of care did not vary between cities (OR 1.15; 95% CI 0.79-1.68; p = 0.4642). In the micro-experiments, improving diagnostic certainty had a positive effect on correct management but not across all quality dimensions. Also, providers delivered idiosyncratically consistent care, repeating all observed actions, including mistakes, approximately 75% of the time. The SP method has limitations: it cannot account for patient mix or care-management practices reflecting more than one patient-provider interaction.ConclusionsQuality of TB care is suboptimal and variable in urban India's private health sector. Addressing this is critical for India's plans to end TB by 2025. For the first time, we have rich measures on representative levels of care quality from 2 cities, which can inform private-sector TB interventions and quality-improvement efforts.

Cytotoxic CD4 (CD4CTX) T cells are emerging as an important component of antiviral and antitumor immunity, but the molecular basis of their development remains poorly understood. In the context of human cytomegalovirus infection, a significant proportion of CD4 T cells displays cytotoxic functions. We observed that the transcriptional program of these cells was enriched in CD8 T cell lineage genes despite the absence of ThPOK downregulation. We further show that establishment of CD4CTX-specific transcriptional and epigenetic programs occurred in a stepwise fashion along the Th1-differentiation pathway. In vitro, prolonged activation of naive CD4 T cells in presence of Th1 polarizing cytokines led to the acquisition of perforin-dependent cytotoxic activity. This process was dependent on the Th1 transcription factor Runx3 and was limited by the sustained expression of ThPOK. This work elucidates the molecular program of human CD4CTX T cells and identifies potential targets for immunotherapy against viral infections and cancer.

BACKGROUND:Despite recent reductions in prevalence, China still faces a substantial tuberculosis (TB) burden, with future progress dependent on the ability of rural providers to appropriately detect and refer TB patients for further care. This study (a) provides a baseline assessment of the ability of rural providers to correctly manage presumptive TB cases; (b) measures the gap between provider knowledge and practice and; (c) evaluates how ongoing reforms of China's health system-characterized by a movement toward "integrated care" and promotion of initial contact with grassroots providers-will affect the care of TB patients. METHODS/FINDINGS:Unannounced standardized patients (SPs) presenting with classic pulmonary TB symptoms were deployed in 3 provinces of China in July 2015. The SPs successfully completed 274 interactions across all 3 tiers of China's rural health system, interacting with providers in 46 village clinics, 207 township health centers, and 21 county hospitals. Interactions between providers and standardized patients were assessed against international and national standards of TB care. Using a lenient definition of correct management as at least a referral, chest X-ray or sputum test, 41% (111 of 274) SPs were correctly managed. Although there were no cases of empirical anti-TB treatment, antibiotics unrelated to the treatment of TB were prescribed in 168 of 274 interactions or 61.3% (95% CI: 55%-67%). Correct management proportions significantly higher at county hospitals compared to township health centers (OR 0.06, 95% CI: 0.01-0.25, p < 0.001) and village clinics (OR 0.02, 95% CI: 0.0-0.17, p < 0.001). Correct management in tests of knowledge administered to the same 274 physicians for the same case was 45 percentage points (95% CI: 37%-53%) higher with 24 percentage points (95% CI: -33% to -15%) fewer antibiotic prescriptions. Relative to the current system, where patients can choose to bypass any level of care, simulations suggest that a system of managed referral with gatekeeping at the level of village clinics would reduce proportions of correct management from 41% to 16%, while gatekeeping at the level of the township hospital would retain correct management close to current levels at 37%. The main limitations of the study are 2-fold. First, we evaluate the management of a one-time new patient presenting with presumptive TB, which may not reflect how providers manage repeat patients or more complicated TB presentations. Second, simulations under alternate policies require behavioral and statistical assumptions that should be addressed in future applications of this method. CONCLUSIONS:There were significant quality deficits among village clinics and township health centers in the management of a classic case of presumptive TB, with higher proportions of correct case management in county hospitals. Poor clinical performance does not arise only from a lack of knowledge, a phenomenon known as the "know-do" gap. Given significant deficits in quality of care, reforms encouraging first contact with lower tiers of the health system can improve efficiency only with concomitant improvements in appropriate management of presumptive TB patients in village clinics and township health centers.

Understanding the functional relevance of DNA variants is essential for all exome and genome sequencing projects. However, current mutagenesis cloning protocols require Sanger sequencing, and thus are prohibitively costly and labor-intensive. We describe a massively-parallel site-directed mutagenesis approach, "Clone-seq", leveraging next-generation sequencing to rapidly and cost-effectively generate a large number of mutant alleles. Using Clone-seq, we further develop a comparative interactome-scanning pipeline integrating high-throughput GFP, yeast two-hybrid (Y2H), and mass spectrometry assays to systematically evaluate the functional impact of mutations on protein stability and interactions. We use this pipeline to show that disease mutations on protein-protein interaction interfaces are significantly more likely than those away from interfaces to disrupt corresponding interactions. We also find that mutation pairs with similar molecular phenotypes in terms of both protein stability and interactions are significantly more likely to cause the same disease than those with different molecular phenotypes, validating the in vivo biological relevance of our high-throughput GFP and Y2H assays, and indicating that both assays can be used to determine candidate disease mutations in the future. The general scheme of our experimental pipeline can be readily expanded to other types of interactome-mapping methods to comprehensively evaluate the functional relevance of all DNA variants, including those in non-coding regions.

Elucidating the molecular basis of human cancers is an extremely complex and challenging task. A wide variety of computational tools and experimental techniques have been used to address different aspects of this characterization. One major hurdle faced by both clinicians and researchers has been to pinpoint the mechanistic basis underlying a wide range of prognostic outcomes for the same type of cancer. Here, we provide an overview of various computational methods that have leveraged different functional genomics data sets to identify molecular signatures that can be used to predict prognostic outcome for various human cancers. Furthermore, we outline challenges that remain and future directions that may be explored to address them.