Assessing the impact of COVID-19 management on the workload of human resources working in India's National Tuberculosis Elimination Program.

BACKGROUND: In 1993, WHO declared tuberculosis (TB) as a global health emergency considering 10 million people are battling TB, of which 30% are undiagnosed annually. In 2020 the COVID-19 pandemic took an unprecedented toll on health systems in every country. Public health staff already engaged in TB control and numerous other departments were additionally tasked with managing COVID-19, stretching human resource (HR) capacity beyond its limits. As part of an assessment of HR involved in TB control in India, The World Bank Group and partners conducted an analysis of the impact of COVID-19 on TB human resources for health (HRH) workloads, with the objective of describing the extent to which TB-related activities could be fulfilled and hypothesizing on future HR requirements to meet those needs. METHODS: The study team conducted a Workload Indicators and Staffing Needs (WISN) analysis according to standard WHO methodology to classify the workloads of priority cadres directly or indirectly involved in TB control activities as over-, adequately or under-worked, in 18 districts across seven states in India. Data collection was done via telephone interviews, and questions were added regarding the proportion of time dedicated to COVID-19 related tasks. We carried out quantitative analysis to describe the time allocated to COVID-19 which otherwise would have been spent on TB activities. We also conducted key informant interviews (KII) with key TB program staff about HRH planning and task-shifting from TB to COVID-19. RESULTS: Workload data were collected from 377 respondents working in or together with India's Central TB Division (CTD). 73% of all respondents (n = 270) reported carrying out COVID-19 tasks. The average time spent on COVID-19 tasks was 4 h / day (n = 72 respondents). Multiple cadres highly instrumental in TB screening and diagnosis, in particular community outreach (ASHA) workers and CBNAAT/TrueNAAT laboratory technicians working at peripheral, block and district levels, were overworked, and spending more than 50% of their time on COVID-19 tasks, reducing time for TB case-finding. Qualitative interviews with laboratory technicians revealed that PCR machines previously used for TB testing were repurposed for COVID-19 testing. CONCLUSIONS: The devastating impact of COVID-19 on HR capacity to conduct TB case-finding in India, as in other settings, cannot be overstated. Our findings provide clear evidence that NTEP human resources did not have time or essential material resources to carry out TB tasks during the COVID pandemic without doing substantial overtime and/or compromising on TB service delivery. To minimize disruptions to routine health services such as TB amidst future emerging infectious diseases, we would do well, during periods of relative calm and stability, to strategically map out how HRH lab staff, public health resources, such as India's Health and Wellness Centers and public health cadre, and public-private sector collaboration can most optimally absorb shocks to the health system.

Melatonin serves as a novel treatment in bladder fibrosis through TGF-ß1/Smad and EMT.

BACKGROUND: Melatonin (MEL) is an indole amine molecule primarily produced in the pineal gland. Melatonin has been shown in numerous studies to have antifibrotic effects on the kidney, liver, and other organs. However, it is still unclear how melatonin works in bladder fibrosis. We explored how melatonin affects animals with bladder fibrosis and the underlying mechanisms. MATERIALS AND METHODS: MEL was used to treat human bladder smooth muscle cells (HBdSMCs) after they were stimulated with transforming growth factor-ß1 (TGF-ß1) in vitro. Proteomic analysis and bioinformatic analysis of the altered expression of these proteins were subsequently performed on HBdSMCs from the different processing methods. To construct an in vivo bladder fibrosis model, we injected protamine sulfate (PS) and lipopolysaccharide (LPS) twice a week into the rat bladder for six weeks. After two weeks of PS/LPS treatment, the mice in the treatment group were treated with MEL (20 mg/kg/d) for 4 weeks. Finally, we detected the expression of fibrosis markers from different perspectives. The TGF-ß1/Smad pathway and epithelial-mesenchymal transition (EMT) in cell and bladder tissues were also identified. Further proteomic analysis was also performed. RESULTS: In vitro, we found that TGF-ß1 treatment enhanced the expression of the fibrosis markers collagen III and α-SMA in HBdSMCs. E-cadherin expression decreased while the TGF-ß1/Smad pathway was activated. Vimentin and N-cadherin expression was also elevated at the same time. Similar findings were observed in the LPS group. After MEL treatment, the expression of collagen III and α-SMA decreased, the expression of E-cadherin increased, and the expression of vimentin and N-cadherin also decreased. According to our quantitative proteomics analysis, CCN1 and SQLE may be important proteins involved in the development of bladder fibrosis. MEL decreased the expression of these genes, leading to the relief of bladder fibrosis. Bioinformatics analysis revealed that the extracellular space structure related to metabolic pathways, actin filament binding, and stress fibers can serve as a pivotal focus in the management of fibrosis. CONCLUSION: Melatonin attenuates bladder fibrosis by blocking the TGF-ß1/Smad pathway and EMT. CCN1 appears to be a possible therapeutic target for bladder fibrosis.

CT-based radiomics: predicting early outcomes after percutaneous transluminal renal angioplasty in patients with severe atherosclerotic renal artery stenosis.

This study aimed to comprehensively evaluate non-contrast computed tomography (CT)-based radiomics for predicting early outcomes in patients with severe atherosclerotic renal artery stenosis (ARAS) after percutaneous transluminal renal angioplasty (PTRA). A total of 52 patients were retrospectively recruited, and their clinical characteristics and pretreatment CT images were collected. During a median follow-up period of 3.7 mo, 18 patients were confirmed to have benefited from the treatment, defined as a 20% improvement from baseline in the estimated glomerular filtration rate. A deep learning network trained via self-supervised learning was used to enhance the imaging phenotype characteristics. Radiomics features, comprising 116 handcrafted features and 78 deep learning features, were extracted from the affected renal and perirenal adipose regions. More features from the latter were correlated with early outcomes, as determined by univariate analysis, and were visually represented in radiomics heatmaps and volcano plots. After using consensus clustering and the least absolute shrinkage and selection operator method for feature selection, five machine learning models were evaluated. Logistic regression yielded the highest leave-one-out cross-validation accuracy of 0.780 (95%CI: 0.660-0.880) for the renal signature, while the support vector machine achieved 0.865 (95%CI: 0.769-0.942) for the perirenal adipose signature. SHapley Additive exPlanations was used to visually interpret the prediction mechanism, and a histogram feature and a deep learning feature were identified as the most influential factors for the renal signature and perirenal adipose signature, respectively. Multivariate analysis revealed that both signatures served as independent predictive factors. When combined, they achieved an area under the receiver operating characteristic curve of 0.888 (95%CI: 0.784-0.992), indicating that the imaging phenotypes from both regions complemented each other. In conclusion, non-contrast CT-based radiomics can be leveraged to predict the early outcomes of PTRA, thereby assisting in identifying patients with ARAS suitable for this treatment, with perirenal adipose tissue providing added predictive value.

Policy gaps in addressing market failures and intervention misalignments in tuberculosis control: prospects for improvement in China, India, and Indonesia.

India, Indonesia, and China are the top three countries with the highest tuberculosis (TB) burden. To achieve the end TB target, we analyzed policy gaps in addressing market failures as well as misalignments between National TB Programs (NTP) and health insurance policies in TB control in three countries. In India and Indonesia, we found insufficient incentives to engage private practitioners or to motivate them to improve service quality. In addition, ineffective supervision of practice and limited coverage of drugs or diagnostics was present in all three countries. The major policy misalignment identified in all three countries is that while treatment guidelines encourage outpatient treatment for drug-sensitive patients, the national health insurance scheme covers primarily inpatient services. We therefore advocate for better alignment of TB control programs and broader universal health coverage (UHC) programs to leverage additional resources from national health insurance programs to improve the effective coverage of TB care.

Artificial intelligence in liver imaging: methods and applications.

Liver disease is regarded as one of the major health threats to humans. Radiographic assessments hold promise in terms of addressing the current demands for precisely diagnosing and treating liver diseases, and artificial intelligence (AI), which excels at automatically making quantitative assessments of complex medical image characteristics, has made great strides regarding the qualitative interpretation of medical imaging by clinicians. Here, we review the current state of medical-imaging-based AI methodologies and their applications concerning the management of liver diseases. We summarize the representative AI methodologies in liver imaging with focusing on deep learning, and illustrate their promising clinical applications across the spectrum of precise liver disease detection, diagnosis and treatment. We also address the current challenges and future perspectives of AI in liver imaging, with an emphasis on feature interpretability, multimodal data integration and multicenter study. Taken together, it is revealed that AI methodologies, together with the large volume of available medical image data, might impact the future of liver disease care.

Government responses to the COVID-19 pandemic of the Gulf Cooperation Council countries: good practices and lessons for future preparedness.

The COVID-19 pandemic has dramatically threatened the Gulf Cooperation Council (GCC) countries which have a large proportion of foreign workers. The governments of GCC countries have proactively implemented a comprehensive set of policy measures, and up to our knowledge, a systematic analysis of qualitative and quantitative evidence on the government response is still lacking. We summarized the GCC countries' government response and quantitatively measured that response using four indexes-the Government Response Index, the Stringency Index, the Vaccine Index, and the Initial Response Index, to analyse their response for future pandemic preparedness. Overall, the government response of all the GCC countries to the COVID-19 pandemic has been comprehensive, stringent, and timely. Notably, the GCC countries have implemented comprehensive vaccine policies. In addition, they have worked actively to protect foreign workers to improve their access to health services and secure their essential living conditions, regardless of their immigrant status. All the GCC countries dynamically adjusted their response to the evolving COVID-19 epidemiological burden and started to relax the stringency of the control policies after the Omicron wave, though the governments had different response magnitudes as measured by the four indexes. These findings have provided several important lessons for future pandemic response and preparedness for countries with similar economic, demographic, and health contexts in (1) prompt actions of containment and closure policies with dynamic adjusting, (2) strengthening health system policies, (3) comprehensive vaccination policies with universal access, (4) equitable and free access to testing, diagnosis, and treatment for all, and (5) strengthening the resilience of health systems.

Breaking boundaries in radiology: redefining AI diagnostics via raw data ahead of reconstruction.

Objective.In the realm of utilizing artificial intelligence (AI) for medical image analysis, the paradigm of 'signal-image-knowledge' has remained unchanged. However, the process of 'signal to image' inevitably introduces information distortion, ultimately leading to irrecoverable biases in the 'image to knowledge' process. Our goal is to skip reconstruction and build a diagnostic model directly from the raw data (signal).Approach. This study focuses on computed tomography (CT) and its raw data (sinogram) as the research subjects. We simulate the real-world process of 'human-signal-image' using the workflow 'CT-simulated data- reconstructed CT,' and we develop a novel AI predictive model directly targeting raw data (RCTM). This model comprises orientation, spatial, and global analysis modules, embodying the fusion of local to global information extraction from raw data. We selected 1994 patients with retrospective cases of solid lung nodules and modeled different types of data.Main results. We employed predefined radiomic features to assess the diagnostic feature differences caused by reconstruction. The results indicated that approximately 14% of the features had Spearman correlation coefficients below 0.8. These findings suggest that despite the increasing maturity of CT reconstruction algorithms, they still introduce perturbations to diagnostic features. Moreover, our proposed RCTM achieved an area under the curve (AUC) of 0.863 in the diagnosis task, showcasing a comprehensive superiority over models constructed from secondary reconstructed CTs (0.840, 0.822, and 0.825). Additionally, the performance of RCTM closely resembled that of models constructed from original CT scans (0.868, 0.878, and 0.866).Significance. The diagnostic and therapeutic approach directly based on CT raw data can enhance the precision of AI models and the concept of 'signal-to-image' can be extended to other types of imaging. AI diagnostic models tailored to raw data offer the potential to disrupt the traditional paradigm of 'signal-image-knowledge', opening up new avenues for more accurate medical diagnostics.

A radiogenomic clinical decision support system to inform individualized treatment in advanced nasopharyngeal carcinoma.

Both concurrent chemoradiotherapy (CCRT) and induction chemotherapy (ICT) followed by CCRT are standard care of advanced nasopharyngeal carcinoma (NPC). However, tailoring personalized treatment is lacking. Herein, we established a radiogenomic clinical decision support system to classify patients into three subgroups according to their predicted disease-free survival (DFS) with CCRT and ICT response. The CCRT-preferred group was suitable for CCRT since they achieved good survival with CCRT, which could not be improved by ICT. The ICT-preferred group was suitable for ICT plus CCRT since they had poor survival with CCRT; additional ICT could afford an improved DFS. The clinical trial-preferred group was suitable for clinical trials since they exhibited poor survival regardless of receiving CCRT or ICT plus CCRT. These findings suggest that our radiogenomic clinical decision support system could identify optimal candidates for CCRT, ICT plus CCRT, and clinical trials, and may thus aid in personalized management of advanced NPC.

Radiomic signatures associated with tumor immune heterogeneity predict survival in locally recurrent nasopharyngeal carcinoma.

BACKGROUND: The prognostic value of traditional clinical indicators for locally recurrent nasopharyngeal carcinoma is limited because of their inability to reflect intratumor heterogeneity. We aimed to develop a radiomic signature to reveal tumor immune heterogeneity and predict survival in locally recurrent nasopharyngeal carcinoma. METHODS: This multicenter, retrospective study included 921 patients with locally recurrent nasopharyngeal carcinoma. A machine learning signature and nomogram based on pretreatment magnetic resonance imaging features were developed for predicting overall survival in a training cohort and validated in 2 independent cohorts. A clinical nomogram and an integrated nomogram were constructed for comparison. Nomogram performance was evaluated by concordance index and receiver operating characteristic curve analysis. Accordingly, patients were classified into risk groups. The biological characteristics and immune infiltration of the signature were explored by RNA-sequencing analysis. RESULTS: The machine learning signature and nomogram demonstrated comparable prognostic ability to a clinical nomogram, achieving concordance indexes of 0.729, 0.718, and 0.731 in the training, internal, and external validation cohorts, respectively. Integration of the signature and clinical variables statistically improved the predictive performance. The proposed signature effectively distinguished patients between risk groups with statistically distinct overall survival rates. Subgroup analysis indicated the recommendation of local salvage treatments for low-risk patients. Exploratory RNA-sequencing analysis revealed differences in interferon response and lymphocyte infiltration between risk groups. CONCLUSIONS: A magnetic resonance imaging-based radiomic signature predicted overall survival more accurately. The proposed signature associated with tumor immune heterogeneity may serve as a valuable tool to facilitate prognostic stratification and guide individualized management for locally recurrent nasopharyngeal carcinoma patients.

The Applications of Artificial Intelligence in Digestive System Neoplasms: A Review.

Importance: Digestive system neoplasms (DSNs) are the leading cause of cancer-related mortality with a 5-year survival rate of less than 20%. Subjective evaluation of medical images including endoscopic images, whole slide images, computed tomography images, and magnetic resonance images plays a vital role in the clinical practice of DSNs, but with limited performance and increased workload of radiologists or pathologists. The application of artificial intelligence (AI) in medical image analysis holds promise to augment the visual interpretation of medical images, which could not only automate the complicated evaluation process but also convert medical images into quantitative imaging features that associated with tumor heterogeneity. Highlights: We briefly introduce the methodology of AI for medical image analysis and then review its clinical applications including clinical auxiliary diagnosis, assessment of treatment response, and prognosis prediction on 4 typical DSNs including esophageal cancer, gastric cancer, colorectal cancer, and hepatocellular carcinoma. Conclusion: AI technology has great potential in supporting the clinical diagnosis and treatment decision-making of DSNs. Several technical issues should be overcome before its application into clinical practice of DSNs.