Indian Journal of Medical Research最新文献

Abstract: From an initial thought of being used as a cellular garbage bin to a promising target for liquid biopsies, the role of exosomes has drastically evolved in just a few years of their discovery in 1983. Exosomes are naturally secreted nano-sized vesicles, abundant in all types of body fluids and can be isolated intact even from the stored biological samples. Being stable carriers of genetic material (cellular DNA, mRNA and miRNA) and having specific cargo (signature content of originating cells), exosomes play a crucial role in pathogenesis and have been identified as a novel source of biomarkers in a variety of disease conditions. Recently exosomes have emerged as a promising 'liquid biopsy tool'and have shown great potential in the field of non-invasive disease diagnostics, prognostics and treatment response monitoring in both communicable as well as non-communicable diseases. However, there are certain limitations to overcome which restrict the use of exosome-based liquid biopsy as a gold standard testing procedure in routine clinical practices. The present review summarizes the current knowledge on the role of exosomes as the liquid biopsy tool in diagnosis, prognosis and treatment response monitoring in communicable and non-communicable diseases and highlights the major limitations, technical advancements and future prospects of the utilization of exosome-based liquid biopsy in clinical interventions.

Background objectives: Expenditure on healthcare is a major concern in the geriatric age group. The current study was carried out to assess the expenditure patterns on medicines utilized in geriatric inpatients.

Methods: An observational study was conducted on 1000 geriatric inpatients, aged ≥60 yr, admitted to the medicine unit. Data were collected regarding demographic characteristics, prescribed medicines, expenditure incurred on medicines, appropriateness of medicines prescribed and adverse drug reactions (ADRs). Appropriateness of the prescribed medicines was determined using the American Geriatrics Society 2015 Updated Beers Criteria.

Results: Geriatric inpatients comprised 41.3 per cent of the total individuals admitted in the ward during the study period. A total of 8366 medicines were prescribed in 127 formulations. The total expenditure on prescribed medicines was INR 1,087,175 with a per capita expenditure of INR 1087.17. Parenteral medicines accounted for 91 per cent of the expenditure on medicines. Maximum expenditure (70%) was incurred on 11.9 per cent of the medicines prescribed. The per capita expenditure was significantly higher in individuals with comorbidities (P=0.03) and those who had a longer duration of hospital stay (P<0.0001). About 28.1 per cent prescriptions were inappropriate. ADRs (140) were observed in 139 (13.9%) inpatients. Individuals with inappropriate medicines prescriptions and ADRs had a longer duration of hospital stay and more number of medicines prescribed.

Interpretation conclusions: Comorbidities, prolonged hospitalization, polypharmacy, inappropriate medicines and parenteral medicines being prescribed contribute to increased expenditure on medicines in geriatric inpatients. In view of the rising number of geriatric inpatients, there is a need to frame a drug policy for them along with surveillance of expenditure on prescribed medicines. This needs to be treated as a priority.

Background objectives: There is limited evidence studying the relationship of liver segmental dose and segmental volume changes. The segmental dose thresholds could potentially allow for segmental regeneration after liver stereotactic body radiation therapy (SBRT). Given improved survival in hepatocellular cancer (HCC) and liver metastases and more salvage therapy options, this has become an important clinical question to explore. This study assesses the impact of liver segmental dose on segmental volume changes (gain or loss) after SBRT.

Methods: Liver segmental contours were delineated on baseline and serial follow up triphasic computed tomography scans. The volumes of total liver and doses to total liver, uninvolved liver and individual segments were noted. A correlation was evaluated between liver/segmental volume and dose using Pearson's correlation. Furthermore, receiver operator's curve (ROC) analysis was performed to find the segmental dose, i.e . predictive for liver volume loss.

Results: A total of 140 non-tumour liver segments were available for analysis in 21 participants. Overall, 13 participants showed loss of overall liver volume and eight showed gain of overall liver volume. The median dose in segments reporting an increase in volume was 9.1 Gy (7-36 Gy). The median dose in segments losing volume was 15.5 Gy (1-49 Gy). On ROC analysis, segmental dose >11 Gy was associated with volume loss. On univariate analysis, only liver segmental dose contributed to a significant segmental volume loss.

Interpretation conclusions: We propose from the findings of this study that in SBRT for large hepatocellular cancer or liver metastases, liver segments should be individually delineated. Furthermore, 3-5 liver segments may be preferentially subjected to <9 Gy to facilitate hepatocyte regeneration. Preferential sparing of uninvolved liver segments may improve outcomes in liver stereotaxyas lower segmental doses were associated with liver regeneration. This may have implications on future liver SBRT planning where segmental doses may be as important as the mean dose.

Background objectives: The clinical course of COVID-19 and its prognosis are influenced by both viral and host factors. The objectives of this study were to develop a nationwide platform to investigate the molecular epidemiology of SARS-CoV-2 (Severe acute respiratory syndrome Corona virus 2) and correlate the severity and clinical outcomes of COVID-19 with virus variants.

Methods: A nationwide, longitudinal, prospective cohort study was conducted from September 2021 to December 2022 at 14 hospitals across the country that were linked to a viral sequencing laboratory under the Indian SARS-CoV-2 Genomics Consortium. All participants (18 yr and above) who attended the hospital with a suspicion of SARS-CoV-2 infection and tested positive by the reverse transcription-PCR method were included. The participant population consisted of both hospitalized as well as outpatients. Their clinical course and outcomes were studied prospectively. Nasopharyngeal samples collected were subjected to whole genome sequencing to detect SARS-CoV-2 variants.

Results: Of the 4972 participants enrolled, 3397 provided samples for viral sequencing and 2723 samples were successfully sequenced. From this, the evolution of virus variants of concern including Omicron subvariants which emerged over time was observed and the same reported here. The mean age of the study participants was 41 yr and overall 49.3 per cent were female. The common symptoms were fever and cough and 32.5 per cent had comorbidities. Infection with the Delta variant evidently increased the risk of severe COVID-19 (adjusted odds ratio: 2.53, 95% confidence interval: 1.52, 4.2), while Omicron was milder independent of vaccination status. The independent risk factors for mortality were age >65 yr, presence of comorbidities and no vaccination.

Interpretation conclusions: The authors believe that this is a first-of-its-kind study in the country that provides real-time data of virus evolution from a pan-India network of hospitals closely linked to the genome sequencing laboratories. The severity of COVID-19 could be correlated with virus variants with Omicron being the milder variant.

Background objectives: Discovery of new antibiotics is the need of the hour to treat infectious diseases. An ever-increasing repertoire of multidrug-resistant pathogens poses an imminent threat to human lives across the globe. However, the low success rate of the existing approaches and technologies for antibiotic discovery remains a major bottleneck. In silico methods like machine learning (ML) deem more promising to meet the above challenges compared with the conventional experimental approaches. The goal of this study was to create ML models that may be used to successfully predict new antimicrobial compounds.

Methods: In this article, we employed eight different ML algorithms namely, extreme gradient boosting, random forest, gradient boosting classifier, deep neural network, support vector machine, multilayer perceptron, decision tree, and logistic regression. These models were trained using a dataset comprising 312 antibiotic drugs and a negative set of 936 non-antibiotic drugs in a five-fold cross validation approach.

Results: The top four ML classifiers (extreme gradient boosting, random forest, gradient boosting classifier and deep neural network) were able to achieve an accuracy of 80 per cent and above during the evaluation of testing and blind datasets.

Interpretation conclusions: We aggregated the top performing four models through a soft-voting technique to develop an ensemble-based ML method and incorporated it into a freely accessible online prediction server named ABDpred ( http://clinicalmedicinessd.com.in/abdpred/ ).