Improved method for stress detection using bio-sensor technology and machine learning algorithms.

Maintaining an optimal stress level is vital in our lives, yet many individuals struggle to identify the sources of their stress. As emotional stability and mental awareness become increasingly important, wearable medical technology has gained popularity in recent years. This technology enables real-time monitoring, providing medical professionals with crucial physiological data to enhance patient care. Current stress-detection methods, such as ECG, BVP, and body movement analysis, are limited by their rigidity and susceptibility to noise interference. To overcome these limitations, we introduce STRESS-CARE, a versatile stress detection sensor employing a hybrid approach. This innovative system utilizes a sweat sensor, cutting-edge context identification methods, and machine learning algorithms. STRESS-CARE processes sensor data and models environmental fluctuations using an XG Boost classifier. By combining these advanced techniques, we aim to revolutionize stress detection, offering a more adaptive and robust solution for improved stress management and overall well-being.•In the proposed method, we introduce a state-of-the-art stress detection device with Galvanic Skin Response (GSR) sweat sensors, outperforming traditional Electrocardiogram (ECG) methods while remaining non-invasive•Integrating machine learning, particularly XG-Boost algorithms, enhances detection accuracy and reliability.•This study sheds light on noise context comprehension for various wearable devices, offering crucial guidance for optimizing stress detection in multiple contexts and applications.

Extracellular Vesicles in Triple-Negative Breast Cancer: Immune Regulation, Biomarkers, and Immunotherapeutic Potential.

Triple-negative breast cancer (TNBC) is an aggressive subtype accounting for ~10-20% of all human BC and is characterized by the absence of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) amplification. Owing to its unique molecular profile and limited targeted therapies, TNBC treatment poses significant challenges. Unlike other BC subtypes, TNBC lacks specific molecular targets, rendering endocrine therapies and HER2-targeted treatments ineffective. The chemotherapeutic regimen is the predominant systemic treatment modality for TNBC in current clinical practice. However, the efficacy of chemotherapy in TNBC is variable, with response rates varying between a wide range of patients, and the emerging resistance further adds to the difficulties. Furthermore, TNBC exhibits a higher mutational burden and is acknowledged as the most immunogenic of all BC subtypes. Consequently, the application of immune checkpoint inhibition has been investigated in TNBC, yielding promising outcomes. Recent evidence identified extracellular vesicles (EVs) as an important contributor in the context of TNBC immunotherapy. In view of the extraordinary ability of EVs to transfer bioactive molecules, such as proteins, lipids, DNA, mRNAs, and small miRNAs, between the cells, EVs are considered a promising diagnostic biomarker and novel drug delivery system among the prospects for immunotherapy. The present review provides an in-depth understanding of how EVs influence TNBC progression, its immune regulation, and their contribution as a predictive biomarker for TNBC. The final part of the review focuses on the recent key advances in immunotherapeutic strategies for better understanding the complex interplay between EVs and the immune system in TNBC and further developing EV-based targeted immunotherapies.

Microbiome based approaches for the degradation of polycyclic aromatic hydrocarbons (PAHs): A current perception.

Globally, polycyclic aromatic hydrocarbons (PAHs) pollution is primarily driven by their release into the air through various combustion processes, including burning fossil fuels such as coal, oil, and gas in motor vehicles, power plants, and industries, as well as burning organic matter like wood, tobacco, and food in fireplaces, cigarettes, and grills. Apart from anthropogenic pollution sources, PAHs also occur naturally in crude oil, and their potential release during oil extraction, refining processes, and combustion further contributes to contamination and pollution concerns. PAHs are resistant and persistent in the environment because of their inherent features, viz., heterocyclic aromatic ring configurations, hydrophobicity, and thermostability. A wide range of microorganisms have been found to be effective degraders of these recalcitrant contaminants. The presence of hydrocarbons as a result of numerous anthropogenic activities is one of the primary environmental concerns. PAHs are found in soil, water, and the air, making them ubiquitous in nature. The presence of PAHs in the environment creates a problem, as their presence has a detrimental effect on humans and animals. For a variety of life forms, PAH pollutants are reported to be toxic, carcinogenic, mutation-inducing, teratogenic, and immune toxicogenics. Degradation of PAHs via biological activity is an extensively used approach in which diverse microorganisms (fungal, algal, clitellate, and protozoan) and plant species and their derived composites are utilized as biocatalysts and biosurfactants. Some microbes have the ability to transform and degrade these PAHs, allowing them to be removed from the environment. The goal of this review is to provide a critical overview of the existing understanding of PAH biodegradation. It also examines current advances in diverse methodologies for PAH degradation in order to shed light on fundamental challenges and future potential.

Beyond Macromolecules: Extracellular Vesicles as Regulators of Inflammatory Diseases.

Inflammation is the defense mechanism of the immune system against harmful stimuli such as pathogens, toxic compounds, damaged cells, radiation, etc., and is characterized by tissue redness, swelling, heat generation, pain, and loss of tissue functions. Inflammation is essential in the recruitment of immune cells at the site of infection, which not only aids in the elimination of the cause, but also initiates the healing process. However, prolonged inflammation often brings about several chronic inflammatory disorders; hence, a balance between the pro- and anti-inflammatory responses is essential in order to eliminate the cause while producing the least damage to the host. A growing body of evidence indicates that extracellular vesicles (EVs) play a major role in cell-cell communication via the transfer of bioactive molecules in the form of proteins, lipids, DNA, RNAs, miRNAs, etc., between the cells. The present review provides a brief classification of the EVs followed by a detailed description of how EVs contribute to the pathogenesis of various inflammation-associated diseases and their implications as a therapeutic measure. The latter part of the review also highlights how EVs act as a bridging entity in blood coagulation disorders and associated inflammation. The findings illustrated in the present review may open a new therapeutic window to target EV-associated inflammatory responses, thereby minimizing the negative outcomes.

IL6Myc mouse is an immunocompetent model for the development of aggressive multiple myeloma.

Multiple Myeloma (MM) is a plasma cell neoplasm originating in the bone marrow and is the second most common blood cancer in the United States. One challenge in understanding the pathogenesis of MM and improving treatment is a lack of immunocompetent mouse models. We previously developed the IL6Myc mouse that generates plasmacytomas at 100% penetrance that phenotypically resemble aggressive MM. Using comprehensive genomic analysis, we found that the IL6Myc tumors resemble aggressive MM by RNA and protein expression. We also found that IL6Myc tumors accumulated fusions and missense mutations in genes that overlap significantly with human myeloma, indicating that the mouse is good model for studying disease etiology. Lastly, we derived cell lines from IL6Myc tumors that express cell surface markers typical of MM and readily engraft into mice, home to the bone marrow, and induce osteolytic disease. The cell lines may be useful in developing immunotherapies directed against BAFF-R and TACI, though not BCMA, and may also be a good model for studying dexamethasone resistance. These data indicate that the IL6Myc model is useful for studying development of aggressive MM and for developing new treatments against such forms of the disease.

Unlocking multidimensional cancer therapeutics using geometric data science.

Personalised approaches to cancer therapeutics primarily involve identification of patient sub-populations most likely to benefit from targeted drugs. Such a stratification has led to plethora of designs of clinical trials that are often too complex due to the need for incorporating biomarkers and tissue types. Many statistical methods have been developed to address these issues; however, by the time such methodology is available research in cancer has moved on to new challenges and therefore in order to avoid playing catch-up it is necessary to develop new analytic tools alongside. One of the challenges facing cancer therapy is to effectively and appropriately target multiple therapies for sensitive patient population based on a panel of biomarkers across multiple cancer types, and matched future trial designs. We present novel geometric methods (mathematical theory of hypersurfaces) to visualise complex cancer therapeutics data as multidimensional, as well as geometric representation of oncology trial design space in higher dimensions. The hypersurfaces are used to describe master protocols, with application to a specific example of a basket trial design for melanoma, and thus setup a framework for further incorporating multi-omics data as multidimensional therapeutics.

Stem cell therapy: a novel approach against emerging and re-emerging viral infections with special reference to SARS-CoV-2.

The past several decades have witnessed the emergence and re-emergence of many infectious viral agents, flaviviruses, influenza, filoviruses, alphaviruses, and coronaviruses since the advent of human deficiency virus (HIV). Some of them even become serious threats to public health and have raised major global health concerns. Several different medicinal compounds such as anti-viral, anti-malarial, and anti-inflammatory agents, are under investigation for the treatment of these viral diseases. These therapies are effective improving recovery rates and overall survival of patients but are unable to heal lung damage caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Therefore, there is a critical need to identify effective treatments to combat this unmet clinical need. Due to its antioxidant and immunomodulatory properties, stem cell therapy is considered a novel approach to regenerate damaged lungs and reduce inflammation. Stem cell therapy uses a heterogeneous subset of regenerative cells that can be harvested from various adult tissue types and is gaining popularity due to its prodigious regenerative potential as well as immunomodulatory and anti-inflammatory properties. These cells retain expression of cluster of differentiation markers (CD markers), interferon-stimulated gene (ISG), reduce expression of pro-inflammatory cytokines and, show a rapid proliferation rate, which makes them an attractive tool for cellular therapies and to treat various inflammatory and viral-induced injuries. By examining various clinical studies, this review demonstrates positive considerations for the implications of stem cell therapy and presents a necessary approach for treating virally induced infections in patients.

Emerging Roles and Potential Applications of Non-Coding RNAs in Cervical Cancer.

Cervical cancer (CC) is a preventable disease using proven interventions, specifically prophylactic vaccination, pervasive disease screening, and treatment, but it is still the most frequently diagnosed cancer in women worldwide. Patients with advanced or metastatic CC have a very dismal prognosis and current therapeutic options are very limited. Therefore, understanding the mechanism of metastasis and discovering new therapeutic targets are crucial. New sequencing tools have given a full visualization of the human transcriptome's composition. Non-coding RNAs (NcRNAs) perform various functions in transcriptional, translational, and post-translational processes through their interactions with proteins, RNA, and even DNA. It has been suggested that ncRNAs act as key regulators of a variety of biological processes, with their expression being tightly controlled under physiological settings. In recent years, and notably in the past decade, significant effort has been made to examine the role of ncRNAs in a variety of human diseases, including cancer. Therefore, shedding light on the functions of ncRNA will aid in our better understanding of CC. In this review, we summarize the emerging roles of ncRNAs in progression, metastasis, therapeutics, chemo-resistance, human papillomavirus (HPV) regulation, metabolic reprogramming, diagnosis, and as a prognostic biomarker of CC. We also discussed the role of ncRNA in the tumor microenvironment and tumor immunology, including cancer stem cells (CSCs) in CC. We also address contemporary technologies such as antisense oligonucleotides, CRISPR-Cas9, and exosomes, as well as their potential applications in targeting ncRNAs to manage CC.

CRISPR/Cas9 system in breast cancer therapy: advancement, limitations and future scope.

Cancer is one of the major causes of mortality worldwide, therefore it is considered a major health concern. Breast cancer is the most frequent type of cancer which affects women on a global scale. Various current treatment strategies have been implicated for breast cancer therapy that includes surgical removal, radiation therapy, hormonal therapy, chemotherapy, and targeted biological therapy. However, constant effort is being made to introduce novel therapies with minimal toxicity. Gene therapy is one of the promising tools, to rectify defective genes and cure various cancers. In recent years, a novel genome engineering technology, namely the clustered regularly interspaced short palindromic repeat (CRISPR)-associated protein-9 (Cas9) has emerged as a gene-editing tool and transformed genome-editing techniques in a wide range of biological domains including human cancer research and gene therapy. This could be attributed to its versatile characteristics such as high specificity, precision, time-saving and cost-effective methodologies with minimal risk. In the present review, we highlight the role of CRISPR/Cas9 as a targeted therapy to tackle drug resistance, improve immunotherapy for breast cancer.

Classification of Glaucoma Stages Using Image Empirical Mode Decomposition from Fundus Images.

One of the most prevalent causes of visual loss and blindness is glaucoma. Conventionally, instrument-based tools are employed for glaucoma screening. However, they are inefficient, time-consuming, and manual. Hence, computerized methodologies are needed for fast and accurate diagnosis of glaucoma. Therefore, we proposed a Computer-Aided Diagnosis (CAD) method for the classification of glaucoma stages using Image Empirical Mode decomposition (IEMD). In this study, IEMD is applied to decompose the preprocessed fundus photographs into different Intrinsic Mode Functions (IMFs) to capture the pixel variations. Then, the significant texture-based descriptors have been computed from the IMFs. A dimensionality reduction approach called Principal Component Analysis (PCA) has been employed to pick the robust descriptors from the retrieved feature set. We used the Analysis of Variance (ANOVA) test for feature ranking. Finally, the LS-SVM classifier has been employed to classify glaucoma stages. The proposed CAD system achieved a classification accuracy of 94.45% for the binary classification on the RIM-ONE r12 database. Our approach demonstrated better glaucoma classification performance than the existing automated systems.

Correction: Parashar et al. Patient-Derived Ovarian Cancer Spheroids Rely on PI3K-AKT Signaling Addiction for Cancer Stemness and Chemoresistance. Cancers 2022, 14, 958.

The authors would like to correct the author byline to include Dr [...].

Efficiency of a randomized confirmatory basket trial design constrained to control the family wise error rate by indication.

Basket trials pool histologic indications sharing molecular pathophysiology, improving development efficiency. Currently, basket trials have been confirmatory only for exceptional therapies. Our previous randomized basket design may be generally suitable in the resource-intensive confirmatory phase, maintains high power even with modest effect sizes, and provides nearly k-fold increased efficiency for k indications, but controls false positives for the pooled result only. Since family wise error rate by indications may sometimes be required, we now simulate a variant of this basket design controlling family wise error rate at 0.025k, the total family wise error rate of k separate randomized trials. We simulated this modified design under numerous scenarios varying design parameters. Only designs controlling family wise error rate and minimizing estimation bias were allowable. Optimal performance results when k=3,4. We report efficiency (expected # true positives/expected sample size) relative to k parallel studies, at 90% power ("uncorrected") or at the power achieved in the basket trial ("corrected," because conventional designs could also increase efficiency by sacrificing power). Efficiency and power (percentage active indications identified) improve with a higher percentage of initial indications active. Up to 92% uncorrected and 38% corrected efficiency improvement is possible. Even under family wise error rate control, randomized confirmatory basket trials substantially improve development efficiency. Initial indication selection is critical.

Venous Thromboembolism Rate in Patients With Bladder Cancer According to the Type of Treatment: A Systematic Review.

Bladder cancer (BC) is classified as a high-risk tumour type for venous thromboembolism (VTE). VTE presents an extra challenge in the management of patients with cancer, given the increase in morbidity and mortality on having both conditions. To summarise the contemporary evidence on the VTE rate in patients with BC according to the stage, type of anti-cancer treatment and highlight VTE rate in the UK and other countries. A systematic review was carried out, and an electronic search for publications between January 2000 and November 2021 was done. Studies recording VTE in BC patients were included, whilst paediatric patients, case reports, studies reporting on a mix of arterial and venous thrombosis, studies reporting DVT or PE only and recorded hospitalised VTE only were excluded. The rate of VTE, country of origin, risk factors and thromboprophylaxis duration for VTE in BC patients were identified. A total of 38 papers met the search criteria. All publications were original research papers (cohort studies). The overall VTE rate in patients with BC was estimated at 1.9% to 4.7%. For those patients undergoing cystectomy, the VTE rate ranged from 3% to 17.6%; however, the VTE rate in the metastatic stage of BC patients ranged from 3.1% to 5.1%. The rates of VTE in BC patients are high, further increased by interventions such as surgery and chemotherapy. Thromboprophylaxis measures should be optimised. This review highlighted the fact that the VTE rate in BC varies between studies due to the heterogeneity of risk factors reported.

Tumor Derived Extracellular Vesicles Drive T Cell Exhaustion in Tumor Microenvironment through Sphingosine Mediated Signaling and Impacting Immunotherapy Outcomes in Ovarian Cancer.

SPHK1 (sphingosine kinase-1) catalyzes the phosphorylation of sphingosine to sphingosine-1-phosphate (S1P), is found to be highly expressed in solid tumors. Here, extracellular vesicles (EVs) are identified as the key transporters of SPHK1 to the tumor microenvironment. Consequently, SPHK1-packaged EVs elevate S1P levels in the tumor microenvironment, where S1P appears as an immunosuppressive agent. However, the exact mechanism of how S1P mediates its immunosuppressive effects in cancer is not understood. It is investigated that S1P can induce T cell exhaustion. S1P can also upregulate programmed death ligand-1 (PDL-1) expression through E2F1-mediated transcription. Notably, an SPHK1 inhibitor PF543 improves T cell-mediated cytotoxicity. Furthermore, combining PF543 with an anti-PD-1 antibody reduces tumor burden and metastasis more effectively than PF543 alone in vivo. These data demonstrate a previously unrecognized mechanism of how SPHK1-packaged EVs contribute to the progression of ovarian cancer and thus present the potential clinical application of inhibiting SPHK1/S1P signaling to improve immune checkpoint blockage (anti-PD-1 antibody) therapy in ovarian cancer.

Using Omics to Study Leprosy, Tuberculosis, and Other Mycobacterial Diseases.

Mycobacteria are members of the Actinomycetales order, and they are classified into one family, Mycobacteriaceae. More than 20 mycobacterial species cause disease in humans. The Mycobacterium group, called the Mycobacterium tuberculosis complex (MTBC), has nine closely related species that cause tuberculosis in animals and humans. TB can be detected worldwide and one-fourth of the world's population is contaminated with tuberculosis. According to the WHO, about two million dies from it, and more than nine million people are newly infected with TB each year. Mycobacterium tuberculosis (M. tuberculosis) is the most potential causative agent of tuberculosis and prompts enormous mortality and morbidity worldwide due to the incompletely understood pathogenesis of human tuberculosis. Moreover, modern diagnostic approaches for human tuberculosis are inefficient and have many lacks, while MTBC species can modulate host immune response and escape host immune attacks to sustain in the human body. "Multi-omics" strategies such as genomics, transcriptomics, proteomics, metabolomics, and deep sequencing technologies could be a comprehensive strategy to investigate the pathogenesis of mycobacterial species in humans and offer significant discovery to find out biomarkers at the early stage of disease in the host. Thus, in this review, we attempt to understand an overview of the mission of "omics" approaches in mycobacterial pathogenesis, including tuberculosis, leprosy, and other mycobacterial diseases.

Patient-Derived Ovarian Cancer Spheroids Rely on PI3K-AKT Signaling Addiction for Cancer Stemness and Chemoresistance.

Ovarian cancer is the most lethal gynecological malignancy among women worldwide and is characterized by aggressiveness, cancer stemness, and frequent relapse due to resistance to platinum-based therapy. Ovarian cancer cells metastasize through ascites fluid as 3D spheroids which are more resistant to apoptosis and chemotherapeutic agents. However, the precise mechanism as an oncogenic addiction that makes 3D spheroids resistant to apoptosis and chemotherapeutic agents is not understood. To study the signaling addiction mechanism that occurs during cancer progression in patients, we developed an endometrioid subtype ovarian cancer cell line named 'MCW-OV-SL-3' from the ovary of a 70-year-old patient with stage 1A endometrioid adenocarcinoma of the ovary. We found that the cell line MCW-OV-SL-3 exhibits interstitial duplication of 1q (q21-q42), where this duplication resulted in high expression of the PIK3C2B gene and aberrant activation of PI3K-AKT-ERK signaling. Using short tandem repeat (STR) analysis, we demonstrated that the cell line exhibits a unique genetic identity compared to existing ovarian cancer cell lines. Notably, the MCW-OV-SL-3 cell line was able to form 3D spheroids spontaneously, which is an inherent property of tumor cells when plated on cell culture dishes. Importantly, the tumor spheroids derived from the MCW-OV-SL-3 cell line expressed high levels of c-Kit, PROM1, ZEB1, SNAI, VIM, and Twist1 compared to 2D monolayer cells. We also observed that the hyperactivation of ERK and PI3K/AKT signaling in these cancer cells resulted in resistance to cisplatin. In summary, the MCW-OV-SL3 endometrioid cell line is an excellent model to study the mechanism of cancer stemness and chemoresistance in endometrioid ovarian cancer.

Do Support Vector Machines Play a Role in Stratifying Patient Population Based on Cancer Biomarkers?

Biomarkers are known to be the key driver behind targeted cancer therapies by either stratifying the patients into risk categories or identifying patient subgroups most likely to benefit. However, the ability of a biomarker to stratify patients relies heavily on the type of clinical endpoint data being collected. Of particular interest is the scenario when the biomarker involved is a continuous one where the challenge is often to identify cut-offs or thresholds that would stratify the population according to the level of clinical outcome or treatment benefit. On the other hand, there are well-established Machine Learning (ML) methods such as the Support Vector Machines (SVM) that classify data, both linear as well as non-linear, into subgroups in an optimal way. SVMs have proven to be immensely useful in data-centric engineering and recently researchers have also sought its applications in healthcare. Despite their wide applicability, SVMs are not yet in the mainstream of toolkits to be utilised in observational clinical studies or in clinical trials. This research investigates the very role of SVMs in stratifying the patient population based on a continuous biomarker across a variety of datasets. Based on the mathematical framework underlying SVMs, we formulate and fit algorithms in the context of biomarker stratified cancer datasets to evaluate their merits. The analysis reveals their superior performance for certain data-types when compared to other ML methods suggesting that SVMs may have the potential to provide a robust yet simplistic solution to stratify real cancer patients based on continuous biomarkers, and hence accelerate the identification of subgroups for improved clinical outcomes or guide targeted cancer therapies.

RNA-binding protein FXR1 drives cMYC translation by recruiting eIF4F complex to the translation start site.

Fragile X-related protein-1 (FXR1) gene is highly amplified in patients with ovarian cancer, and this amplification is associated with increased expression of both FXR1 mRNA and protein. FXR1 expression directly associates with the survival and proliferation of cancer cells. Surface sensing of translation (SUnSET) assay demonstrates that FXR1 enhances the overall translation in cancer cells. Reverse-phase protein array (RPPA) reveals that cMYC is the key target of FXR1. Mechanistically, FXR1 binds to the AU-rich elements (ARE) present within the 3' untranslated region (3'UTR) of cMYC and stabilizes its expression. In addition, the RGG domain in FXR1 interacts with eIF4A1 and eIF4E proteins. These two interactions of FXR1 result in the circularization of cMYC mRNA and facilitate the recruitment of eukaryotic translation initiation factors to the translation start site. In brief, we uncover a mechanism by which FXR1 promotes cMYC levels in cancer cells.