Predicting Alzheimer's Disease Progression Using a Versatile Sequence-Length-Adaptive Encoder-Decoder LSTM Architecture.

Detecting Alzheimer's disease (AD) accurately at an early stage is critical for planning and implementing disease-modifying treatments that can help prevent the progression to severe stages of the disease. In the existing literature, diagnostic test scores and clinical status have been provided for specific time points, and predicting the disease progression poses a significant challenge. However, few studies focus on longitudinal data to build deep-learning models for AD detection. These models are not stable to be relied upon in real medical settings due to a lack of adaptive training and testing. We aim to predict the individual's diagnostic status for the next six years in an adaptive manner where prediction performance improves with the number of patient visits. This study presents a Sequence-Length Adaptive Encoder-Decoder Long Short-Term Memory (SLA-ED LSTM) deep-learning model on longitudinal data obtained from the Alzheimer's Disease Neuroimaging Initiative archive. In the suggested approach, decoder LSTM dynamically adjusts to accommodate variations in training sequence length and inference length rather than being constrained to a fixed length. We evaluated the model performance for various sequence lengths and found that for inference length one, sequence length nine gives the highest average test accuracy and area under the receiver operating characteristic curves of 0.920 and 0.982, respectively. This insight suggests that data from nine visits effectively captures meaningful cognitive status changes and is adequate for accurate model training. We conducted a comparative analysis of the proposed model against state-of-the-art methods, revealing a significant improvement in disease progression prediction over the previous methods.

Structure-Activity Relationship Study of Biselyngbyolide B Reveals Mitochondrial Fission-Induced Cytotoxicity in Cancer.

A systematic structure-activity relationship study of the potent anticancer marine macrolide biselyngbyolide B has been accomplished. A total of 11 structural variants of the parent natural product, of which 2 are natural analogues, have been studied against a human colorectal carcinoma cell line. The requisite functional units of the parent molecule responsible for the cytotoxic activities have been disclosed. Biselyngbyolide C, one of the natural analogues of biselyngbyolide B, has been studied in depth to explore its molecular mechanism. Interestingly, the in vitro data demonstrated an induction of dynamin-related protein 1-mediated mitochondrial fission and reactive oxygen species production which led to activation of ASK1/P38/JNK-mediated apoptosis in colon cancer cells as an important pathway for biselyngbyolide B-mediated cytotoxicity. Notably, this study revealed that a macrolide participated in mitochondrial fission to promote apoptosis of cancer cells, providing new insight.

Corrigendum: Genomic surveillance and phylodynamic analyses reveal the emergence of novel mutations and co-mutation patterns within SARS-CoV-2 variants prevalent in India.

[This corrects the article DOI: 10.3389/fmicb.2021.703933.].

Discovery and Development of Quinazolinones and Quinazolinediones for Ameliorating Nonalcoholic Fatty Liver Disease (NAFLD) by Modulating COP1-ATGL Axis.

E3 ubiquitin ligase, Constitutive Photomorphogenic 1 (COP1) regulates turnover of Adipose Triglyceride Lipase (ATGL), the rate-limiting lipolytic enzyme. Genetic perturbation in the COP1-ATGL axis disrupts lipid homeostasis, leading to liver steatosis. Using drug development strategies, we herein report quinazolinone and quinazolinedione based modulators for COP1-ATGL axis. Systematic SAR studies and subsequent optimization were performed by incorporating relevant functional groups at the N1, N3, C5, and C6 positions of both scaffolds. Compounds' efficacy was evaluated by multiple biological assays and ADME profiling. The lead compound 86 could increase ATGL protein expression, reduce ATGL ubiquitination and COP1 autoubiquitination, and diminish lipid accumulation in hepatocytes in the nanomolar range. Oral administration of 86 abrogated triglyceride accumulation and resolved fibrosis in preclinical Nonalcoholic Fatty Liver Disease (NAFLD) model. The study thus establishes quinazolinedione as a viable chemotype to therapeutically modulate the activity of COP1 and ATGL in relevant clinical contexts.