Estimation of stature from anthropometry of hand: an interesting autopsy based study in Madhya Pradesh, India

Background: Height of a person is an important parameter for the identification. Height or stature estimation is central dogma in anthropo-forensic examination. The human hand, which is the most used and versatile part of the body is of great scientific importance to investigators in the field of anthropometry, forensic pathology, orthopedic surgery and ergonomics. The hand length was found to be the most reliable alternative that can be used as a basis for estimating age-related loss in height. The hand length could also be used to predict body weight status and body surface area independent of the sex of the individual.Methods: The present cross sectional prospective study was carried out in mortuary of department of Forensic Medicine, Mahatma Gandhi Memorial Medical College and M.Y. Hospital, Indore (M.P.), India during study period from September 2014 to September 2015. The study was conducted on 250 deceased male and 250 deceased female individuals. The measurements were taken using standard anthropometric measuring instruments in centimeters to the nearest millimeters. All the measurements were recorded on a predesigned pretested proforma. Anthropometric measurements were taken as per the standard protocol.Results: The mean age of the male and female study subjects was38.472±13.28 years and 34.728±10.33 years respectively. Male to female ratio was 1:1. Mean stature in male subjects was 163.5±5.21 cm.  Mean stature in female subjects was 155.69±10.12 cm. In male study subjects, mean hand length on right side was more than mean hand length on left side. In female study subjects, mean hand length on right side was more than on left side. In male study subjects, hand breadth (HB) on right side was more than on left side. Average HB in male subjects was 8.39±0.203 cm. In female study subjects, hand breadth on right side was more than on left side.Conclusions: The findings of the present study can be used as baseline information for other population based studies in the study area.

Selection and Characterization of Green Propellants for Micro-Resistojets

The number of launches of nano-and pico-satellites has significantly increased over the past decade. Miniaturized subsystems, such as micropropulsion, for these classes of spacecraft are rapidly evolving and, in particular, micro-resistojets have shown great potential of applicability. One of the key points to address in the development of such devices is the propellants selection, since it directly influences the performance. This paper presents a methodology for the selection and characterization of fluids that are suitable for use as propellants in two micro-resistojet concepts: vaporizing liquid microresistojet (VLM) and the low-pressure micro-resistojet (LPM). In these concepts, the propellant is heated by a nonchemical energy source, in this case an electrical resistance. In total 95 fluids have been investigated including conventional and unconventional propellants. A feasibility assessment step is carried out following a trade-off using a combination of the analytical hierarchy process (AHP) and the Pugh matrix. A final list of nine best-scoring candidates has been analyzed in depth with respect to the thermal characteristics involved in the process, performance parameters, and safety issues. For both concepts, water has been recognized as a very promising candidate along with other substances such as ammonia and methanol

First national survey of anti-tuberculosis drug resistance in Azerbaijan and risk factors analysis.

SETTING: Civilian population of the Republic of Azerbaijan. OBJECTIVES: To determine patterns of anti-tuberculosis drug resistance among new and previously treated pulmonary tuberculosis (TB) cases, and explore their association with socio-demographic and clinical characteristics. DESIGN: National cross-sectional survey conducted in 2012-2013. RESULTS: Of 789 patients (549 new and 240 previously treated) who met the enrolment criteria, 231 (42%) new and 146 (61%) previously treated patients were resistant to any anti-tuberculosis drug; 72 (13%) new and 66 (28%) previously treated patients had multidrug-resistant TB (MDR-TB). Among MDR-TB cases, 38% of new and 46% of previously treated cases had pre-extensively drug-resistant TB (pre-XDR-TB) or XDR-TB. In previously treated cases, 51% of those who had failed treatment had MDR-TB, which was 15 times higher than in relapse cases (OR 15.2, 95%CI 6-39). The only characteristic significantly associated with MDR-TB was a history of previous treatment (OR 3.1, 95%CI 2.1-4.7); for this group, history of incarceration was an additional risk factor for MDR-TB (OR 2.8, 95%CI 1.1-7.4). CONCLUSION: Azerbaijan remains a high MDR-TB burden country. There is a need to implement countrywide control and innovative measures to accelerate early diagnosis of drug resistance in individual patients, improve treatment adherence and strengthen routine surveillance of drug resistance

Identification and prediction of Parkinson's disease subtypes and progression using machine learning in two cohorts.

The clinical manifestations of Parkinson's disease (PD) are characterized by heterogeneity in age at onset, disease duration, rate of progression, and the constellation of motor versus non-motor features. There is an unmet need for the characterization of distinct disease subtypes as well as improved, individualized predictions of the disease course. We used unsupervised and supervised machine learning methods on comprehensive, longitudinal clinical data from the Parkinson's Disease Progression Marker Initiative (n = 294 cases) to identify patient subtypes and to predict disease progression. The resulting models were validated in an independent, clinically well-characterized cohort from the Parkinson's Disease Biomarker Program (n = 263 cases). Our analysis distinguished three distinct disease subtypes with highly predictable progression rates, corresponding to slow, moderate, and fast disease progression. We achieved highly accurate projections of disease progression 5 years after initial diagnosis with an average area under the curve (AUC) of 0.92 (95% CI: 0.95 ± 0.01) for the slower progressing group (PDvec1), 0.87 ± 0.03 for moderate progressors, and 0.95 ± 0.02 for the fast-progressing group (PDvec3). We identified serum neurofilament light as a significant indicator of fast disease progression among other key biomarkers of interest. We replicated these findings in an independent cohort, released the analytical code, and developed models in an open science manner. Our data-driven study provides insights to deconstruct PD heterogeneity. This approach could have immediate implications for clinical trials by improving the detection of significant clinical outcomes. We anticipate that machine learning models will improve patient counseling, clinical trial design, and ultimately individualized patient care

Managing toxicities associated with immune checkpoint inhibitors: consensus recommendations from the Society for Immunotherapy of Cancer (SITC) Toxicity Management Working Group.

Cancer immunotherapy has transformed the treatment of cancer. However, increasing use of immune-based therapies, including the widely used class of agents known as immune checkpoint inhibitors, has exposed a discrete group of immune-related adverse events (irAEs). Many of these are driven by the same immunologic mechanisms responsible for the drugs\u27 therapeutic effects, namely blockade of inhibitory mechanisms that suppress the immune system and protect body tissues from an unconstrained acute or chronic immune response. Skin, gut, endocrine, lung and musculoskeletal irAEs are relatively common, whereas cardiovascular, hematologic, renal, neurologic and ophthalmologic irAEs occur much less frequently. The majority of irAEs are mild to moderate in severity; however, serious and occasionally life-threatening irAEs are reported in the literature, and treatment-related deaths occur in up to 2% of patients, varying by ICI. Immunotherapy-related irAEs typically have a delayed onset and prolonged duration compared to adverse events from chemotherapy, and effective management depends on early recognition and prompt intervention with immune suppression and/or immunomodulatory strategies. There is an urgent need for multidisciplinary guidance reflecting broad-based perspectives on how to recognize, report and manage organ-specific toxicities until evidence-based data are available to inform clinical decision-making. The Society for Immunotherapy of Cancer (SITC) established a multidisciplinary Toxicity Management Working Group, which met for a full-day workshop to develop recommendations to standardize management of irAEs. Here we present their consensus recommendations on managing toxicities associated with immune checkpoint inhibitor therapy

Multi-modality machine learning predicting Parkinson's disease

Personalized medicine promises individualized disease prediction and treatment. The convergence of machine learning (ML) and available multimodal data is key moving forward. We build upon previous work to deliver multimodal predictions of Parkinson's disease (PD) risk and systematically develop a model using GenoML, an automated ML package, to make improved multi-omic predictions of PD, validated in an external cohort. We investigated top features, constructed hypothesis-free disease-relevant networks, and investigated drug-gene interactions. We performed automated ML on multimodal data from the Parkinson's progression marker initiative (PPMI). After selecting the best performing algorithm, all PPMI data was used to tune the selected model. The model was validated in the Parkinson's Disease Biomarker Program (PDBP) dataset. Our initial model showed an area under the curve (AUC) of 89.72% for the diagnosis of PD. The tuned model was then tested for validation on external data (PDBP, AUC 85.03%). Optimizing thresholds for classification increased the diagnosis prediction accuracy and other metrics. Finally, networks were built to identify gene communities specific to PD. Combining data modalities outperforms the single biomarker paradigm. UPSIT and PRS contributed most to the predictive power of the model, but the accuracy of these are supplemented by many smaller effect transcripts and risk SNPs. Our model is best suited to identifying large groups of individuals to monitor within a health registry or biobank to prioritize for further testing. This approach allows complex predictive models to be reproducible and accessible to the community, with the package, code, and results publicly available