Immunotherapy represents a paradigm shift in oncology, rooted in a century of evolving scientific understanding and clinical application. From the pioneering use of Coley’s toxins in the late nineteenth century to the introduction of cytokine-based interventions, the trajectory of immunotherapeutic approaches has paralleled advancements in immunology and molecular biology. This review comprehensively examines the historical development and progressive refinement of immunotherapy for cancer, charting the transition from non-specific immune stimulation to targeted immune modulation. Central to this discussion are the sophisticated mechanisms by which tumour cells evade immune detection and destruction. These include downregulation of antigen presentation machinery, secretion of immunosuppressive cytokines, recruitment of regulatory T cells and myeloid-derived suppressor cells, and exploitation of immune checkpoint pathways, particularly CTLA-4 and PD-1/PD-L1 axes. The advent of immune checkpoint inhibitors has yielded durable clinical responses in diverse malignancies, substantiating their role as foundational agents in cancer therapy. Nonetheless, both primary and acquired resistance to immune checkpoint inhibition remain significant clinical obstacles. Resistance mechanisms are multifactorial, involving tumour-intrinsic genetic alterations, modulation of the tumour microenvironment, and adaptive changes in immune cell phenotypes. Contemporary research endeavors are directed at overcoming these barriers, including the optimization of combinatorial regimens, development of next-generation checkpoint modulators, tumour-specific vaccines, and the integration of adoptive cell therapies. Future directions in cancer immunotherapy are poised to leverage advances in systems biology, genomics, and single-cell technologies to individualize interventions and enhance therapeutic efficacy. Ultimately, a comprehensive delineation of tumour-immune interactions will underpin the next generation of rational, effective, and durable cancer immunotherapies.

At the nexus of signal processing and machine learning (ML), silent speech recognition (SSR) has evolved as a game-changing technology that allows for communication without audible voice. This study offers a thorough overview of SSR, tracing its evolution from early waveform analysis to the most recent ML methods. We start by examining current SSR techniques using ML and determining the essential conditions for efficient SSR systems. After that, we look at the datasets and data collection techniques currently employed in SSR research, highlighting the difficulties posed by the variety of articulatory movements and the scarcity of data. Examining state-of-the-art SSR frameworks, the paper covers important topics such signal processing, feature extraction, ML techniques for decoding and optimizing and assessing the performance of SSR models. We emphasize how deep learning (DL) and ML models have evolved to increase SSR resilience and accuracy. The field's proposed procedures are examined, with an emphasis on sophisticated feature extraction and classification methods. Modern SSR techniques are compared in terms of performance, highlighting the advantages and disadvantages of different models. There is also discussion of ethical issues, especially those pertaining to privacy and consent. The integration of multimodal information—visual cues, electromyography signals, and neuroimaging data—to improve SSR systems is covered in this work. We investigate the functions of transfer learning and domain adaptation in handling cross-subject variability. Lastly, the study offers suggestions and future prospects for SSR research, providing practitioners, engineers, and academics with a road map. As SSR continues to push the frontiers of human–machine interaction, our study aims to increase our collective understanding of the technological advances and societal effects of SSR in the ML age.

The accurate diagnosis of small-cell lung cancer (SCLC) is crucial, as treatment strategies differ from those of other lung cancers. This systematic review aims to identify proteins differentially expressed in SCLC compared to normal lung tissue, evaluating their potential utility in diagnosing and prognosing the disease. Additionally, the study identifies proteins differentially expressed between SCLC and large cell neuroendocrine carcinoma (LCNEC), aiming to discover biomarkers distinguishing between these two subtypes of neuroendocrine lung cancers. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive search was conducted across PubMed/MEDLINE, Scopus, Embase, and Web of Science databases. Studies reporting proteomics information and confirming SCLC and/or LCNEC through histopathological and/or cytopathological examination were included, while review articles, non-original articles, and studies based on animal samples or cell lines were excluded. The initial search yielded 1705 articles, and after deduplication and screening, 16 articles were deemed eligible. These studies revealed 117 unique proteins significantly differentially expressed in SCLC compared to normal lung tissue, along with 37 unique proteins differentially expressed between SCLC and LCNEC. In conclusion, this review highlights the potential of proteomics technology in identifying novel biomarkers for diagnosing SCLC, predicting its prognosis, and distinguishing it from LCNEC.

Background/Objectives: Artificial intelligence has made significant strides in healthcare, contributing to diagnosing, treating, monitoring, preventing, and testing various diseases. Despite its broad adoption, clinical consensus on AI’s role in infection control remains uncertain. This scoping review aims to understand the characteristics of AI applications in bacterial infection control. Results: This review examines the characteristics of AI applications in bacterial infection control, analyzing 54 eligible studies across 5 thematic scopes. The search from 3 databases yielded a total of 1165 articles, only 54 articles met the eligibility criteria and were extracted and analyzed. Five thematic scopes were synthesized from the extracted data; countries, aim, type of AI, advantages, and limitations of AI applications in bacterial infection prevention and control. The majority of articles were reported from high-income countries, mainly by the USA. The most common aims are pathogen identification and infection risk assessment. The most common AI used in infection control is machine learning. The commonest reported advantage is predictive modeling and risk assessment, and the commonest disadvantage is generalizability of the models. Methods: This scoping review was developed according to Arksey and O’Malley frameworks. A comprehensive search across PubMed, Embase, and Web of Science was conducted using broad search terms, with no restrictions. Publications focusing on AI in infection control and prevention were included. Citations were managed via EndNote, with initial title and abstract screening by two authors. Data underwent comprehensive narrative mapping and categorization, followed by the construction of thematic scopes. Conclusions: Artificial intelligence applications in infection control need to be strengthened for low-income countries. More efforts should be dedicated to investing in models that have proven their effectiveness in infection control, to maximize their utilization and tackle challenges.

Background and Objectives: Diabetes is linked to a higher risk of urinary tract infections (UTIs) in women, often leading to recurrent antibiotic treatments. Frequent antibiotic use for UTIs can contribute to antimicrobial resistance (AMR), a critical public health threat that increases treatment failure. This study investigated the prevalence of AMR and its associated factors among women with UTIs, comparing those with and without diabetes. Results: The study population had a mean age of 52 years (SD = 23) for the women without diabetes and 68 years (SD = 14) for those with diabetes. Resistance was highest for cefazolin and levofloxacin in the Access and Watch antibiotic groups, while ciprofloxacin was the most frequently prescribed antibiotic. AMR prevalence was 35.7% among the women with diabetes and 21.3% among those without. After adjustment, AMR was significantly associated with both uncomplicated diabetes (OR 1.14, 95% CI 1.08–1.21) and complicated diabetes (OR 1.54, 95% CI 1.45–1.64), as well as with higher numbers of prescribed antibiotics (OR 277.39, 95% CI 253.79–303.17). Methods: Using a cross-sectional cohort from the Physionet database, we analyzed data on 116,902 female participants treated for UTIs, including their antibiotic exposure, diabetes status, comorbidities, and hospital admission details. Antimicrobials were classified per the WHO’s AWaRe criteria. The primary outcome was AMR identified in urine cultures, and the association with diabetes status was evaluated using multivariable logistic regression. Conclusions: Our findings highlight the need for focused antimicrobial stewardship in women with diabetes to reduce the AMR rates in this vulnerable group.

Background: Small cell lung cancer (SCLC) is an extremely aggressive form of lung cancer, characterized by rapid progression and poor survival rates. Despite the importance of early diagnosis, the current diagnostic techniques are invasive and restricted. Methods: This study presents a novel stacking-based ensemble machine learning approach for classifying small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) using metabolomics data. The analysis included 191 SCLC cases, 173 NSCLC cases, and 97 healthy controls. Feature selection techniques identified significant metabolites, with positive ions proving more relevant. Results: For multi-class classification (control, SCLC, NSCLC), the stacking ensemble achieved 85.03% accuracy and 92.47 AUC using Support Vector Machine (SVM). Binary classification (SCLC vs. NSCLC) further improved performance, with ExtraTreesClassifier reaching 88.19% accuracy and 92.65 AUC. SHapley Additive exPlanations (SHAP) analysis revealed key metabolites like benzoic acid, DL-lactate, and L-arginine as significant predictors. Conclusions: The stacking ensemble approach effectively leverages multiple classifiers to enhance overall predictive performance. The proposed model effectively captures the complementary strengths of different classifiers, enhancing the detection of SCLC and NSCLC. This work accentuates the potential of combining metabolomics with advanced machine learning for non-invasive early lung cancer subtype detection, offering an alternative to conventional biopsy methods.