Surgical treatment for non-tuberculous mycobacterial pulmonary disease: analysis of prognostic factors

BACKGROUND: The surgical treatment for non-tuberculous mycobacterial pulmonary disease (NTM-PD) has an important adjunctive role and reported outcomes have been generally good; however, the prognostic factors remain unclear.METHODS: Sixty-one patients with NTM-PD who underwent surgical resection for a therapeutic purpose from January 2000 to March 2017 at five affiliated institutions were enrolled. We explored the factors that influence complications and prognosis by retrospectively referring to the medical records.RESULTS: The mean age of the present cohort was 61.8 ± 11.4 years. The pathogen was Mycobacterium avium complex in 49 patients, M. abscessus in 5. The most common indications were refractory to medication in 39. The surgical techniques employed were lobectomy or further resection in 49, sublobar resection in 8, with video-assisted thoracoscopic surgery in 21. Sputum culture conversion rate was 95.1%. Univariate analysis of factors associated with deterioration revealed significant differences related to age (P = 0.025), pre-operative albumin level (P = 0.001) and development of postoperative complications (P = 0.037), while pre-operative albumin level alone was a significant factor in multivariate analysis (P = 0.009).CONCLUSION: Outcomes after resection were generally good in the present cases. Nutritional status, as indicated by albumin level, may affect prognosis after surgical treatment.

Guava Disease Detection Using Deep Convolutional Neural Networks: A Case Study of Guava Plants

Food production is a growing challenge with the increasing global population. To increase the yield of food production, we need to adopt new biotechnology-based fertilization techniques. Furthermore, we need to improve early prevention steps against plant disease. Guava is an essential fruit in Asian countries such as Pakistan, which is fourth in its production. Several pathological and fungal diseases attack guava plants. Furthermore, postharvest infections might result in significant output losses. A professional opinion is essential for disease analysis due to minor variances in various guava disease symptoms. Farmers’ poor usage of pesticides may result in financial losses due to incorrect diagnosis. Computer-vision-based monitoring is required with developing field guava plants. This research uses a deep convolutional neural network (DCNN)-based data enhancement using color-histogram equalization and the unsharp masking technique to identify different guava plant species. Nine angles from 360∘ were applied to increase the number of transformed plant images. These augmented data were then fed as input into state-of-the-art classification networks. The proposed method was first normalized and preprocessed. A locally collected guava disease dataset from Pakistan was used for the experimental evaluation. The proposed study uses five neural network structures, AlexNet, SqueezeNet, GoogLeNet, ResNet-50, and ResNet-101, to identify different guava plant species. The experimental results proved that ResNet-101 obtained the highest classification results, with 97.74% accuracy.

Proteomic Studies of Blood and Vascular Wall in Atherosclerosis. Review

The review is devoted to the analysis of literature data related to the role of proteomic studies in the study of atherosclerotic cardiovascular diseases. Diagnosis of patients with atherosclerotic plaques before clinical manifestations is an arduous task. The review presents the results of research on the new proteomic potential biomarkers of coronary heart disease, coronary atherosclerosis, acute coronary syndrome, myocardial infarction, carotid artery atherosclerosis. Also, the analysis of literature data on proteomic studies of the vascular wall was carried out. To assess the involvement of proteins in the pathological process of atherosclerosis, it is important to investigate the specific relationships between proteins in the arteries, expression and concentration of proteins. The development of proteomic technologies has made it possible to analyse the number of proteins associated with the development of the disease. Analysis of the proteomic profile of the vascular wall in atherosclerosis can help to detect possible diagnostically significant protein structures or potential biomarkers of the disease and develop novel approaches to the diagnosis of atherosclerosis and its complications.

COVID-19 pandemic and potential of artificial intelligence

Objective To evaluate the potential of artificial intelligence in combating COVID-19 pandemic. Methods PubMed, Embase, Cochrane Library and Google Scholar were searched for the term “Artificial intelligence and COVID-19” up to March 31, 2021. Results Artificial intelligence (AI) is a potential tool to contain the current pandemic. AI can be used in many fields such as early detection and respective diagnosis, supervision of treatment, projection of cases and mortality, contact tracing of individuals, development of drugs and vaccines, reduces workload on health workers, prevention of disease, analysis of mental health of people amid pandemic. Conclusions AI is being updated and being improved, second by second to be able to interpret like actual human minds. This advancement in AI may lead to a completely different future of COVID-19 pandemic where most of the simpler works may be done by AI and only essential works could be done by health workers in order to increase patient care in current scenario of COVID-19 outbreak. But again one of the main constraint is of limited trustworthy and noise free sources of information. So the need for the hour is to make a free data system where most of the analysed data could be available to feed AI, which could effectively halt the current pandemic.

Design and Fabrication of Multichannel PDMS Microfluidic

Microfluidic delivers miniaturized fluidic networks for processing liquids in the microliter range. In the recent years, lab-on-chip (LOC) is become a main tool for point-of-care (POC) diagnostic especially in the medical field. In this paper, we presented a design and fabrication on multi disease analysis using single chip via delivery of fluid with the multiple transducers is the pathway of multi-channel microfluidic based LOC’s. 3 in 1 nano biosensor kit was attached with the microfluidic to produce nano-biolab-on-chip (NBLOC). The multi channels microfluidic chip was designed including the micro channels, one inlet, three outlet and sensor contact area. The microfluidic chip was designed to include multiplex detection for pathogen that consists of multiple channels of simultaneous results. The LOC system was designed using Design Spark Mechanical software and PDMS was used as a medium of the microfluidic. The microfluidic mold and PDMS microfluidic morphological properties have been characterized by using low power microscope (LPM), high power microscope (HPM) and surface profiler. The LOC system physical was experimental by dropping food coloring through the inlet and collecting at the sensor contact area outlet.