A mathematical model to study low-dose metronomic scheduling for chemotherapy.

Metronomic chemotherapy refers to the frequent administration of chemotherapeutic agents at a lower dose and presents an attractive alternative to conventional chemotherapy with encouraging response rates. However, the schedule of the therapy, including the dosage of the drug, is usually based on empiricism. The confounding effects of tumor-endothelial-immune interactions during metronomic administration of drugs have not yet been explored in detail, resulting in an incomplete assessment of drug dose and frequency evaluations. The present study aimed to gain a mechanistic understanding of different actions of metronomic chemotherapy using a mathematical model. We have established an analytical condition for determining the dosage and frequency of the drug depending on its clearance rate for complete tumor elimination. The model also brings forward the immune-mediated clearance of the tumor during the metronomic administration of the chemotherapeutic agent. The results from the global sensitivity analysis showed an increase in the sensitivity of drug and immune-mediated killing factors toward the tumor population during metronomic scheduling. Our results emphasize metronomic scheduling over the maximum tolerated dose (MTD) and define a model-based approach for approximating the optimal schedule of drug administration to eliminate tumors while minimizing harm to the immune cells and the patient's body.

VapC12 ribonuclease toxin modulates host immune response during Mycobacterium tuberculosis infection.

Mechanistic understanding of antibiotic persistence is a prerequisite in controlling the emergence of MDR cases in Tuberculosis (TB). We have reported that the cholesterol-induced activation of VapC12 ribonuclease is critical for disease persistence in TB. In this study, we observed that relative to the wild type, mice infected with ΔvapC12 induced a pro-inflammatory response, had a higher pathogen load, and responded better to the anti-TB treatment. In a high-dose infection model, all the mice infected with ΔvapC12 succumbed early to the disease. Finally, we reported that the above phenotype of ΔvapC12 was dependent on the presence of the TLR4 receptor. Overall, the data suggests that failure of a timely resolution of the early inflammation by the ΔvapC12 infected mice led to hyperinflammation, altered T-cell response and high bacterial load. In conclusion, our findings suggest the role of the VapC12 toxin in modulating the innate immune response of the host in ways that favor the long-term survival of the pathogen inside the host.

Exploration of functional relations among differentially co-expressed genes identifies regulators in glioblastoma.

The conventional computational approaches to investigating a disease confront inherent constraints as they often need to improve in delving beyond protein functional associations and grasping their deeper contextual significance within the disease framework. Such context-specificity can be explored using clinical data by evaluating the change in interaction between the biological entities in different conditions by investigating the differential co-expression relationships. We believe that the integration and analysis of differential co-expression and the functional relationships, primarily focusing on the source nodes, will open novel insights about disease progression as the source proteins could trigger signaling cascades, mostly because they are transcription factors, cell surface receptors, or enzymes that respond instantly to a particular stimulus. A thorough contextual investigation of these nodes could lead to a helpful beginning point for identifying potential causal linkages and guiding subsequent scientific investigations to uncover mechanisms underlying observed associations. Our methodology includes functional protein-protein Interaction (PPI) data and co-expression information and filters functional linkages through a series of critical steps, culminating in the identification of a robust set of regulators. Our analysis identified eleven key regulators-AKT1, BRCA1, CAMK2G, CUL1, FGFR3, KIF3A, NUP210, PRKACB, RAB8A, RPS6KA2 and TGFB3-in glioblastoma. These regulators play a pivotal role in disease classification, cell growth control, and patient survivability and exhibit associations with immune infiltrations and disease hallmarks. This underscores the importance of assessing correlation towards causality in unraveling complex biological insights.

Trends in keratoplasty from central and northern India. Part II: Types of keratoplasty.

PURPOSE: To report the types of keratoplasty and analyze trends over a period of two decades in central and northern India. METHODS: This was a retrospective review of eye bank registries of six tertiary eye care centers located in central and north India from 2005 to 2021. The indications and various keratoplasty procedures were compared between 2005-2012 and 2013-2021 to analyze the trend. RESULTS: A total of 13,223 corneal grafts were performed, of which therapeutic penetrating keratoplasty (TPK) (5719, 43.3%) was the most common procedure, followed by optical penetrating keratoplasty (OPK) (5528, 41.8%), Descemet stripping endothelial keratoplasty (DSEK) (1279, 9.7%), deep anterior lamellar keratoplasty (DALK) (376, 2.8%), Descemet membrane endothelial keratoplasty (DMEK) (215, 1.6%), patch grafts (75, 0.6%), and keratoprostheses (31, 0.2%). Overall, OPK procedures decreased (-14.1%, P < 0.001), but TPK (+3.1%, P < 0.019), DSEK (+7.1%, P < 0.001), and DMEK (+1.9%, P < 0.001) procedures increased. Although there was an increasing trend in the use of DALK (+0.8%, P < 0.083) and keratoprostheses (+0.3%, P = 0.074) procedures, the trends were not statistically significant. CONCLUSION: In keeping with worldwide trends, an increasing trend in lamellar keratoplasties was observed in central and northern India during the past decade. The trend was significant for DSEK. However, as corneal ulcers and scars were the major indications for keratoplasty, TPK and OPK remained the most common procedures.

Bloodborne viral infections: Seroprevalence and relevance of preoperative screening in Indian eye care system - A retrospective study.

PURPOSE: To report the seroprevalence of bloodborne viral infection (BBVI) in patients undergoing ophthalmic surgeries and assess the utility and feasibility of preoperative screening for BBVI in India's current eye care system. METHODS: This retrospective, hospital-based, descriptive study included data from patients undergoing preoperative screening for Hepatitis B virus (HBV), Hepatitis C virus (HCV), and human immunodeficiency virus (HIV) at a tertiary eye care institute from 2018 to 2022. Rapid diagnostic tests (RDTs) were performed on the blood samples after obtaining informed consent from the patients. Seroreactive patients underwent surgery with additional safety precautions. The demographic data and surgical details of these patients were collected and analyzed. ANOVA was used to carry out statistical analysis between groups. During the study period, the number of healthcare workers (HCWs) sustaining needle stick injury (NSI) and accidental sharp injury (ASI) in the operating theater (OT) and details of these injuries were recorded. RESULTS: Samples from 28,563 patients were included. The seroprevalence of BBVI was 1.87% (536/28563). Hepatitis B virus (322, 60.1%) was the most commonly detected infection, and HIV (59, 11%) was the least detected infection. The mean age of the seroreactive patients was 60.3 ± 30.8 years. The incidence of NSI was 0.49/1000 surgeries. Nurses (11) and technicians (4) in the OT sustained maximum NSI. None of the HCWs had seroconversion after NSI. CONCLUSION: The overall seroprevalence of BBVI in the current study is lower than that reported in previously published studies from eye care organizations. Currently, mandatory preoperative screening for BBVI to prevent transmission of these infections to HCWs working in the eye care sector in India appears to be less cost-effective.

Trends in keratoplasty from central and Northern India. Part I: Indications for keratoplasty.

PURPOSE: To report the indications for keratoplasty and analyze trends in two decades in India. METHODS: This was a retrospective review of eye bank registries of six tertiary eye care centers located in central and northern India from 2005 to 2021. The patterns of corneal pathology and changes in trends were analyzed. RESULTS: Between 2005 and 2021, 13223 corneal grafts were performed in the six collaborating tertiary eye care centers. The most common indication for keratoplasty in both decades was corneal ulcer (41.1%), followed by corneal scar (25.5%), failed graft (12.0%), post-cataract surgery corneal edema (11.7%), corneal dystrophies (3.1%), corneal ectasia (1.8%), corneal trauma (0.3%), congenital corneal opacity (0.3%), and others (4.1%). An increasing trend was seen in corneal ulcers, failed grafts, and keratoconus. A reducing trend was seen in corneal scar and aphakic bullous keratopathy. There was no change in pseudophakic bullous keratopathy and Fuchs endothelial corneal dystrophy. CONCLUSION: Corneal ulcers, corneal scars, failed grafts, and post-cataract surgery corneal edema remained the foremost indications for keratoplasty in two decades in India. An increasing trend was seen in corneal ulcers and failed grafts which are of concern as these indications carry a poorer outcome. Capacity building in lamellar keratoplasty techniques is the need of the hour as pseudophakic bullous keratopathy was an important indication, and an increasing trend was noticed in keratoconus.

Preferred practice patterns for keratoconus: An e-survey among Indian cornea specialists.

PURPOSE: This study aimed to report practice patterns in the management of keratoconus in India. METHODS: An e-survey was conducted among members of the Cornea Society of India to assess preferred practice patterns for keratoconus. RESULTS: Of the 912 members, 234 (25.7%) completed the survey. About 88.4% worked in private eye care facilities and 76.1% in cities. Of all the respondents, 91.5% had access to a Scheimpflug-based device or an Orbscan II®; 71.4% of the respondents dispensed scleral contact lenses, and 66.7% dispensed Rose K contact lenses; 92.7% of the respondents performed corneal cross-linking (CXL); and 73.1% of the respondents performed deep anterior lamellar keratoplasty (DALK). A Scheimpflug device or Orbscan II®, Rose K or scleral contact lenses, and CXL were more readily available (P < 0.05) in private eye care facilities. The diagnostic criteria for keratoconus followed by the majority of the respondents (52.1%) were increase in corneal curvature, elevation, and thickness. An increase in keratometry values greater than 0.5 or 1.0D in a 6-month to 1-year period was considered the most favored criterion for keratoconus progression. Epithelium-off, 0.1% riboflavin solution in dextran, and accelerated protocol were the more commonly reported techniques in CXL. About 77.2% of the respondents routinely performed CXL in corneas <400 microns thick. Spectacles (91.0% of the respondents) and topography-guided photorefractive keratectomy (37.6% of the respondents) were, respectively, the preferred optical and surgical methods used to improve vision. CONCLUSION: While keratoconus management in India is comparable to other developed healthcare systems, the concentration of trained corneal surgeons in cities and private eyecare facilities may lead to less equitable distribution of treatment across the country.

Targeting metabolic fluxes reverts metastatic transitions in ovarian cancer.

The formation of spheroids during epithelial ovarian cancer progression is correlated with peritoneal metastasis, disease recurrence, and poor prognosis. Although metastasis has been demonstrated to be driven by metabolic changes in transformed cells, mechanistic associations between metabolism and phenotypic transitions remain ill-explored. We performed quantitative proteomics to identify protein signatures associated with three distinct phenotypic morphologies (2D monolayers and two geometrically distinct three-dimensional spheroidal states) of the high-grade serous ovarian cancer line OVCAR-3. We obtained disease-driving phenotype-specific metabolic reaction modules and elucidated gene knockout strategies to reduce metabolic alterations that could drive phenotypic transitions. Exploring the DrugBank database, we identified and evaluated drugs that could impair such transitions and, hence, cancer progression. Finally, we experimentally validated our predictions by confirming the ability of one of our predicted drugs, the neuraminidase inhibitor oseltamivir, to inhibit spheroidogenesis in three ovarian cancer cell lines without any cytotoxic effects on untransformed stromal mesothelia.

Identification of critical autophagy-related proteins in diabetic retinopathy: A multi-dimensional computational study.

Diabetic retinopathy (DR) is a common consequence of diabetes mellitus and a primary cause of visual impairment in middle-aged and elderly individuals. DR is susceptible to cellular degradation facilitated by autophagy. In this study, we have employed a multi-layer relatedness (MLR) approach to uncover novel autophagy-related proteins involved in DR. The objective of MLR is to determine the relatedness of autophagic and DR proteins by incorporating both expression and prior-knowledge-based similarities. We constructed a prior knowledge-based network and identified the topologically significant novel disease-related candidate autophagic proteins (CAPs). Then, we evaluated their significance in a gene co-expression and a differentially-expressed gene (DEG) network. Finally, we investigated the proximity of CAPs to the known disease-related proteins. Leveraging this methodology, we identified three crucial autophagy-related proteins, TP53, HSAP90AA1, and PIK3R1, which can influence the DR interactome in various layers of heterogeneity of clinical manifestations. They are strongly related to multiple detrimental characteristics of DR, such as pericyte loss, angiogenesis, apoptosis, and endothelial cell migration, and hence may be used to prevent or delay the progression and development of DR. We evaluated one of the identified targets, TP53, in a cell-based model and found that its inhibition resulted in reduced angiogenesis in high glucose condition required to control DR.

Quantitative analysis of the bioenergetics of Mycobacterium tuberculosis along with Glyoxylate cycle as a drug target under inhibition of enzymes using Petri net.

The bacteria Mycobacterium tuberculosis is responsible for the infectious disease Tuberculosis. Targeting the tubercule bacteria is an important challenge in developing the antimycobacterials. The glyoxylate cycle is considered as a potential target for the development of anti-tuberculosis agents, due to its absence in the humans. Humans only possess tricarboxylic acid cycle, while this cycle gets connected to glyoxylate cycle in microbes. Glyoxylate cycle is essential to the Mycobacterium for its growth and survival. Due to this reason, it is considered as a potential therapeutic target for the development of anti-tuberculosis agents. Here, we explore the effect on the behavior of the tricarboxylic acid cycle, glyoxylate cycle and their integrated pathway with the bioenergetics of the Mycobacterium, under the inhibition of key glyoxylate cycle enzymes using Continuous Petri net. Continuous Petri net is a special Petri net used to perform the quantitative analysis of the networks. We first study the tricarboxylic acid cycle and glyoxylate cycle of the tubercule bacteria by simulating its Continuous Petri net model under different scenarios. Both the cycles are then integrated with the bioenergetics of the bacteria and the integrated pathway is again simulated under different conditions. The simulation graphs show the metabolic consequences of inhibiting the key glyoxylate cycle enzymes and adding the uncouplers on the individual as well as integrated pathway. The uncouplers that inhibit the synthesis of adenosine triphosphate, play an important role as anti-mycobacterials. The simulation study done here validates the proposed Continuous Petri net model as compared with the experimental outcomes and also explains the consequences of the enzyme inhibition on the biochemical reactions involved in the metabolic pathways of the mycobacterium.

Role of reinforcement learning for risk-based robust control of cyber-physical energy systems.

Critical infrastructures such as cyber-physical energy systems (CPS-E) integrate information flow and physical operations that are vulnerable to natural and targeted failures. Safe, secure, and reliable operation and control of CPS-E is critical to ensure societal well-being and economic prosperity. Automated control is key for real-time operations and may be mathematically cast as a sequential decision-making problem under uncertainty. Emergence of data-driven techniques for decision making under uncertainty, such as reinforcement learning (RL), have led to promising advances for addressing sequential decision-making problems for risk-based robust CPS-E control. However, existing research challenges include understanding the applicability of RL methods across diverse CPS-E applications, addressing the effect of risk preferences across multiple RL methods, and development of open-source domain-aware simulation environments for RL experimentation within a CPS-E context. This article systematically analyzes the applicability of four types of RL methods (model-free, model-based, hybrid model-free and model-based, and hierarchical) for risk-based robust CPS-E control. Problem features and solution stability for the RL methods are also discussed. We demonstrate and compare the performance of multiple RL methods under different risk specifications (risk-averse, risk-neutral, and risk-seeking) through the development and application of an open-source simulation environment. Motivating numerical simulation examples include representative single-zone and multizone building control use cases. Finally, six key insights for future research and broader adoption of RL methods are identified, with specific emphasis on problem features, algorithmic explainability, and solution stability.

Bistability regulates TNFR2-mediated survival and death of T-regulatory cells.

A subgroup of T cells called T-regulatory cells (Tregs) regulates the body's immune responses to maintain homeostasis and self-tolerance. Tregs are crucial for preventing illnesses like cancer and autoimmunity. However, contrasting patterns of Treg frequency are observed in different autoimmune diseases. The commonality of tumour necrosis factor receptor 2 (TNFR2) defects and decrease in Treg frequency on the onset of autoimmunity demands an in-depth study of the TNFR2 pathway. To unravel this mystery, we need to study the mechanism of cell survival and death in Tregs. Here, we construct an ordinary differential equation (ODE)-based model to capture the mechanism of cell survival and apoptosis in Treg cells via TNFR2 signalling. The sensitivity analysis reveals that the input stimulus, the concentration of tumour necrosis factor (TNF), is the most sensitive parameter for the model system. The model shows that the cell goes into survival or apoptosis via bistable switching. Through hysteretic switching, the system tries to cope with the changing stimuli. In order to understand how stimulus strength and feedback strength influence cell survival and death, we compute bifurcation diagrams and obtain cell fate maps. Our results indicate that the elevated TNF concentration and increased c-Jun N-terminal kinase (JNK) phosphorylation are the major contributors to the death of T-regulatory cells. Biological evidence cements our hypothesis and can be controlled by reducing the TNF concentration. Finally, the system was studied under stochastic perturbation to see the effect of noise on the system's dynamics. We observed that introducing random perturbations disrupts the bistability, reducing the system's bistable region, which can affect the system's normal functioning.

Latent tuberculosis and computational biology: A less-talked affair.

Tuberculosis (TB) is a pervasive and devastating air-borne disease caused by the organisms belonging to the Mycobacterium tuberculosis (Mtb) complex. Currently, it is the global leader in infectious disease-related death in adults. The proclivity of TB to enter the latent state has become a significant impediment to the global effort to eradicate TB. Despite decades of research, latent tuberculosis (LTB) mechanisms remain poorly understood, making it difficult to develop efficient treatment methods. In this review, we seek to shed light on the current understanding of the mechanism of LTB, with an accentuation on the insights gained through computational biology. We have outlined various well-established computational biology components, such as omics, network-based techniques, mathematical modelling, artificial intelligence, and molecular docking, to disclose the crucial facets of LTB. Additionally, we highlighted important tools and software that may be used to conduct a variety of systems biology assessments. Finally, we conclude the article by addressing the possible future directions in this field, which might help a better understanding of LTB progression.

konnect2prot: a web application to explore the protein properties in a functional protein-protein interaction network.

MOTIVATION: The regulation of proteins governs the biological processes and functions and, therefore, the organisms' phenotype. So there is an unmet need for a systematic tool for identifying the proteins that play a crucial role in information processing in a protein-protein interaction (PPI) network. However, the current protein databases and web servers still lag behind to provide an end-to-end pipeline that can leverage the topological understanding of a context-specific PPI network to identify the influential spreaders. Addressing this, we developed a web application, 'konnect2prot' (k2p), which can generate context-specific directional PPI network from the input proteins and detect their biological and topological importance in the network. RESULTS: We pooled together a large amount of ontological knowledge, parsed it down into a functional network, and gained insight into the molecular underpinnings of the disease development by creating a one-stop junction for PPI data. k2p contains both local and global information about a protein, such as protein class, disease mutations, ligands and PDB structure, enriched processes and pathways, multi-disease interactome and hubs and bottlenecks in the directional network. It also identifies spreaders in the network and maps them to disease hallmarks to determine whether they can affect the disease state or not. AVAILABILITY AND IMPLEMENTATION: konnect2prot is freely accessible using the link https://konnect2prot.thsti.in. The code repository is https://github.com/samrat-lab/k2p_bioinfo-2022.

Disaster risk and artificial intelligence: A framework to characterize conceptual synergies and future opportunities.

Artificial intelligence (AI) methods have revolutionized and redefined the landscape of data analysis in business, healthcare, and technology. These methods have innovated the applied mathematics, computer science, and engineering fields and are showing considerable potential for risk science, especially in the disaster risk domain. The disaster risk field has yet to define itself as a necessary application domain for AI implementation by defining how to responsibly balance AI and disaster risk. (1) How is AI being used for disaster risk applications; and how are these applications addressing the principles and assumptions of risk science, (2) What are the benefits of AI being used for risk applications; and what are the benefits of applying risk principles and assumptions for AI-based applications, (3) What are the synergies between AI and risk science applications, and (4) What are the characteristics of effective use of fundamental risk principles and assumptions for AI-based applications? This study develops and disseminates an online survey questionnaire that leverages expertise from risk and AI professionals to identify the most important characteristics related to AI and risk, then presents a framework for gauging how AI and disaster risk can be balanced. This study is the first to develop a classification system for applying risk principles for AI-based applications. This classification contributes to understanding of AI and risk by exploring how AI can be used to manage risk, how AI methods introduce new or additional risk, and whether fundamental risk principles and assumptions are sufficient for AI-based applications.

Antibiotic susceptibility pattern of bacterial isolates from microbial keratitis in North and Central India: A multi centric study.

Purpose: This study was conducted to examine microbiological profile with their antibiotic sensitivity in cases of bacterial keratitis in north and central India to ensure appropriate use of antibiotics. Methods: The microbiology laboratory records of 228 patients with culture-proven bacterial keratitis from 1st January to 31st December 2019 were analyzed. Cultured bacterial isolates were subjected to antimicrobial susceptibility testing to antibiotics commonly used in the treatment of corneal ulcer. Chi-squared or Fisher's exact test were applied to check the significance of difference between the susceptibility levels of antibiotics. Results: The prevalence of Staphylococcus aureus and Pseudomonas aeruginosa-induced keratitis was higher in northern India, whereas that by Streptococcus pneumoniae was more prevalent in central India. In central India, 100% of S. pneumoniae isolates were found to be sensitive to ceftriaxone compared to 79% in northern India (P = 0.017). In comparison to 67% of isolates from north India, 15% of S. aureus isolates from central India were found to be sensitive to ofloxacin (P = 0.009). Similarly, 23% of isolates from central India were found sensitive to amikacin compared to 65% of isolates from north India (P = 0.012). P. aeruginosa isolates from central India were found to be sensitive to ceftazidime in 63% of cases compared to 21% of isolates from north India (P = 0.034). Conclusion: Prevalence of bacteria and their susceptibility to antibiotics are not uniform across geography. Vancomycin remained the most effective drug in all gram-positive coccal infections. S. aureus susceptibility to amikacin was significantly greater in north India. P. aeruginosa showed less susceptibility as compared to previous reports.

Fluoroquinolone resistance in bacterial isolates from ocular infections: Trend in antibiotic susceptibility patterns between 2005-2020.

Purpose: To assess the fluoroquinolone resistance pattern and trends among bacterial isolates from ocular infections over a 16-year period and explore alternative antibiotics in fluoroquinolone-resistant strains. Methods: In this retrospective, longitudinal study, the microbiology laboratory records of patients with different ocular infections diagnosed at an eye institute in central India from 2005-2020 were reviewed to determine the pattern of fluoroquinolone (ciprofloxacin, ofloxacin, gatifloxacin, and moxifloxacin) resistance. Antibiotic susceptibility testing was done using the Kirby-Bauer disc diffusion method. Results: In 725 Gram-positive bacteria, the resistance of ciprofloxacin, ofloxacin, gatifloxacin, and moxifloxacin was 55.9% (95% confidence interval [CI]: 52.2 - 59.6), 42.7% (95% CI: 39.0 - 46.4), 47.6% (95% CI: 43.9 - 51.3), and 45.6% (95% CI: 41.7-49.5), respectively. In 266 Gram-negative bacteria, the resistance of ciprofloxacin, ofloxacin, gatifloxacin, and moxifloxacin was 57.9% (95% CI: 51.9 - 63.9), 56.0% (95% CI: 49.7 - 62.1), 59.9% (95% CI: 53.8 - 66.0), and 74.3% (95% CI: 68.3 - 80.2), respectively. A declining trend in resistance to ciprofloxacin (P < 0.001), ofloxacin (P < 0.001), and moxifloxacin (P < 0.001) was seen in Gram-positive bacteria, whereas a reduction in resistance to only moxifloxacin (P = 0.04) was seen in Gram-negative bacteria. In fluoroquinolone-resistant Gram-positive bacteria, cefuroxime exhibited the highest susceptibility, whereas in fluoroquinolone-resistant Gram-negative bacteria, colistin exhibited the highest susceptibility. Conclusion: Fluoroquinolone resistance was high among bacteria from ocular infections in central India, but a declining trend in resistance to some of the fluoroquinolones was observed in recent times. Cefuroxime and colistin emerged as alternatives in fluoroquinolone-resistant Gram-positive and Gram-negative bacterial infections, respectively.

Understanding noise in cell signalling in the prospect of drug-targets.

The introduction of noise to signals can alter central regulatory switches of cellular processes leading to diseases. Noise is inherently present in the cellular signalling system and plays a decisive role in the input-output (I/O) relation. The current study aims to understand the noise tolerance of motif structures in the cell signalling processes. The vulnerability of a node to noise could be a significant factor in causing signalling error and need to be controlled. We developed stochastic differential equation (SDE) based mathematical models for different network motifs with two nodes and studied the association between motif structure and signal-noise relation. A two-dimensional parameter space analysis on motif sensitivity with noise and input signal variation was performed to classify and rank the motifs. Identifying sensitive motifs and their high druggability infers their significance in screening potential drug-target candidates. Finally, we proposed a theoretical framework to identify nodes from a network as potential drug targets. We applied this mathematical formalism to three cancer networks to identify drug-targets and validated them with existing databases.