Shashanka P. Ashili

Background: Accurate human leukocyte antigen (HLA) genotyping is a critical step in the implementation of neoantigen peptide-based cancer immunotherapy. Existing computational tools for HLA genotyping using high-throughput sequencing data often lack sufficient accuracy when used individually. Employing an ensemble of multiple software tools and sequencing sources can potentially address this limitation; however, this approach is hindered by increased processing time. Methods: We evaluated an ensemble method that utilizes four HLA-genotyping software tools applied to three sequencing sources—tumor exome, normal exome, and tumor RNA-seq—from the same individual to achieve high-confidence HLA genotyping. To reduce processing time, we incorporated an HLA region-specific FASTQ read-filtering strategy. With this protocol we also provide a Docker implementation of the FASTQ read-filtering pipeline and a software tool for the analysis of HLA genotypes. Results: Consensus HLA genotypes for HLA class I alleles, derived from filtered FASTQ files, showed complete concordance with those obtained from unfiltered original FASTQ files. The use of filtered FASTQ files significantly reduced the time required for HLA genotyping. Conclusions: These findings demonstrate the utility of the proposed HLA genotyping approach in achieving rapid and high-confidence two-field HLA genotyping.

PURPOSE: Accurate human leukocyte antigen (HLA) typing is an essential step for designing peptide vaccines used in the personalized neoantigen peptide vaccine immunotherapy (PNPVT) in patients with cancer. The reasons for variation in the patient response to PNPVT are yet unknown. One of the reasons could be the somatic changes in the HLA genes in the cancer cells. The objective of the present research was to analyze the somatic status of HLA class I genes in cancer tissue through integrative genomic analysis and to identify high-confidence subset of potentially functional cancer somatic HLA class I genotype relevant to PNPVT. PATIENTS AND METHODS: Whole-exome (paired tumor-normal) and RNAseq (tumor) paired-end sequencing data from 24 patients with cancer were used for the analysis. The genotyping of HLA class I was performed using four HLA typing software tools. To assess the functional status of HLA class I genes in the cancer tissue, we analyzed somatic mutation, HLA gene loss of heterozygosity, and chromosome 6 copy loss status in cancer exome data. RESULTS: Somatic mutations in HLA genes were detected in the tumor data of five patients, and somatic HLA gene loss of heterozygosity was identified in the tumor data of five patients. Complete or partial chromosome 6 copy loss was detected in eight patient samples. CONCLUSION: The results indicate that HLA class I genes may get affected by somatic changes in cancer tissue, and assessment of the somatic status of the HLA genotype should be performed in the cancer tissues. The results provide robust rational for removal of mutated or lost HLAs from the personalized neoantigen peptide prediction pipeline to potentially increase the efficacy of the PNPVT. Further functional studies are needed to assess the impact of HLA gene mutations/loss on PNPVT outcomes.

Oral squamous cell carcinoma (OSCC) is an invasive and highly malignant cancer with significant morbidity and mortality. Existing treatments including surgery, chemotherapy and radiation have poor ...

In diabetes, possibly the most significant site of microvascular damage is the kidney. Due to diabetes and/or other co-morbidities, such as hypertension and age-related nephron loss, a significant number of people with diabetes suffer from kidney diseases. Improved diabetic care can reduce the prevalence of diabetic nephropathy (DN); however, innovative treatment approaches are still required. MicroRNA-21 (miR-21) is one of the most studied multipotent microRNAs (miRNAs), and it has been linked to renal fibrosis and exhibits significantly altered expression in DN. Targeting miR-21 offers an advantage in DN. Currently, miR-21 is being pharmacologically silenced through various methods, all of which are in early development. In this review, we summarize the role of miR-21 in the molecular pathogenesis of DN and several therapeutic strategies to use miR-21 as a therapeutic target in DN. The existing experimental interventions offer a way to rectify the lower miRNA levels as well as to reduce the higher levels. Synthetic miRNAs also referred to as miR-mimics, can compensate for abnormally low miRNA levels. Furthermore, strategies like oligonucleotides can be used to alter the miRNA levels. It is reasonable to target miR-21 for improved results because it directly contributes to the pathological processes of kidney diseases, including DN.

Traumatic brain injury (TBI) is a significant cause of mortality and morbidity worldwide resulting from falls, car accidents, sports, and blast injuries. TBI is characterized by severe, life-threat... Plain language summary Traumatic brain injury (TBI) is a leading cause of death and disability worldwide due to falls, car accidents, sports and blast injuries. TBI causes severe, life-threatening ... Tweetable abstract Traumatic brain injury (TBI) causes severe consequences, but no current therapies target its complex pathophysiology. Our review examines the immune response's role in tissue rep...

The presence of missing values in a time-series dataset is a very common and well-known problem. Various statistical and machine learning methods have been developed to overcome this problem, with the aim of filling in the missing values in the data. However, the performances of these methods vary widely, showing a high dependence on the type of data and correlations within the data. In our study, we performed some of the well-known imputation methods, such as expectation maximization, k-nearest neighbor, iterative imputer, random forest, and simple imputer, to impute missing data obtained from smart, wearable health trackers. In this manuscript, we proposed the use of data binning for imputation. We showed that the use of data binned around the missing time interval provides a better imputation than the use of a whole dataset. Imputation was performed for 15 min and 1 h of continuous missing data. We used a dataset with different bin sizes, such as 15 min, 30 min, 45 min, and 1 h, and we carried out evaluations using root mean square error (RMSE) values. We observed that the expectation maximization algorithm worked best for the use of binned data. This was followed by the simple imputer, iterative imputer, and k-nearest neighbor, whereas the random forest method had no effect on data binning during imputation. Moreover, the smallest bin sizes of 15 min and 1 h were observed to provide the lowest RMSE values for the majority of the time frames during the imputation of 15 min and 1 h of missing data, respectively. Although applicable to digital health data, we think that this method will also find applicability in other domains.

The increasing usage of smart wearable devices has made an impact not only on the lifestyle of the users, but also on biological research and personalized healthcare services. These devices, which carry different types of sensors, have emerged as personalized digital diagnostic tools. Data from such devices have enabled the prediction and detection of various physiological as well as psychological conditions and diseases. In this review, we have focused on the diagnostic applications of wrist-worn wearables to detect multiple diseases such as cardiovascular diseases, neurological disorders, fatty liver diseases, and metabolic disorders, including diabetes, sleep quality, and psychological illnesses. The fruitful usage of wearables requires fast and insightful data analysis, which is feasible through machine learning. In this review, we have also discussed various machine-learning applications and outcomes for wearable data analyses. Finally, we have discussed the current challenges with wearable usage and data, and the future perspectives of wearable devices as diagnostic tools for research and personalized healthcare domains.

Wearable devices use sensors to evaluate physiological parameters, such as the heart rate, pulse rate, number of steps taken, body fat and diet. The continuous monitoring of physiological parameters offers a potential solution to assess personal healthcare. Identifying outliers or anomalies in heart rates and other features can help identify patterns that can play a significant role in understanding the underlying cause of disease states. Since anomalies are present within the vast amount of data generated by wearable device sensors, identifying anomalies requires accurate automated techniques. Given the clinical significance of anomalies and their impact on diagnosis and treatment, a wide range of detection methods have been proposed to detect anomalies. Much of what is reported herein is based on previously published literature. Clinical studies employing wearable devices are also increasing. In this article, we review the nature of the wearables-associated data and the downstream processing methods for detecting anomalies. In addition, we also review supervised and un-supervised techniques as well as semi-supervised methods that overcome the challenges of missing and un-annotated healthcare data.

We present the design, fabrication and characterization of multiple micro-pocket lid arrays used in live single cell metabolic analysis. In previous work we reported a platform for quantifying single cell oxygen consumption rates realized using a fused silica deep wet etching process. Here we extend that work to a dual-depth wet etching process for microfabrication of multiple sensor trapping (MST) lid arrays. Each lid comprises multiple micro-pockets. Oxygen, pH, other extra-cellular sensors, and reference dye were deposited in the pockets. In order to achieve simultaneous monitoring of multiple metabolic parameters, the lid array serves to hermetically seal arrays of microwells, each containing a single cell. The dual-depth etching process we developed can be easily applied to other glass-based microfabrication purposes requiring dual- or multiple-depth microstructures.