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PubMed2026

Effects of H-coil TMS on suicidality in major depression: A secondary analysis of data from a multisite randomized trial comparing accelerated to once-a-day stimulation.

Suicide is the 10th leading cause of death in US adults. Standard once-daily repetitive transcranial magnetic stimulation (rTMS) can reduce suicidal ideation. Yet, antidepressant and anti-suicidal effects often take several weeks to emerge, while rapid improvement is often required. Accelerated TMS has been proposed as a strategy to hasten therapeutic response. A recent FDA-regulated multicenter trial evaluated accelerated intermittent theta burst Deep TMS with the H1-coil versus standard high-frequency Deep TMS in MDD. Both groups demonstrated high remission and response rates for depression, with the accelerated protocol showing non-inferiority and a shorter time to remission. The goal of this exploratory secondary analysis was to evaluate the impact of these two H-coil TMS dosing paradigms on suicidal ideation. The Scale for Suicide Ideation (SSI), as well as suicidality items of HDRS, MADRS and CUDOS were collected and analyzed. On all scales, both accelerated and standard Deep TMS protocols were associated with meaningful reductions in suicidality. The accelerated protocol achieved a faster onset of improvement. Comparison between the timeline of improvement in suicidality and in overall depressive symptoms found a trend for faster improvement in suicidality, especially with the accelerated protocol. These findings highlight the importance of treatment frequency in determining time to clinical benefit and support the use of scalable accelerated protocols for patients requiring more rapid symptom relief.

Humans↗

PubMed2026

The efficacy of non-invasive brain stimulation interventions in obsessive-compulsive disorder management: A network meta-analysis of randomized controlled trials.

Non-invasive brain stimulation (NIBS) has been widely used as an alternative treatment for obsessive compulsive disorder (OCD). However, the most effective NIBS parameters are unclear. To compare the efficacy of NIBS in OCD. We conducted a systematic review and network meta-analyses (NMA) to combine direct and indirect comparisons of NIBS.Systematic searches were conducted in Cochrane CENTRAL, EMBASE, PubMed, and Web of Science from inception to June 20, 2025. Forty-two randomized sham-controlled trials (n = 1456) were included. All statistical analyses were conducted with R statistical software. Bayesian NMAs mainly using the BUGSnet package and gemtc package. Five NIBS protocols produced statistically significant reductions in Yale-Brown Obsessive Compulsive Scale (Y-BOCS) scores compared with sham stimulation: high-frequency rTMS over the FzFCz (Hf-rTMS-FzFCz; MD -11.77, 95% CrI -20.62 to -3.09), low-frequency rTMS over F3F4 (Lf-rTMS-F3F4; MD -9.93, 95% CrI -18.07 to -1.65), low-frequency rTMS over FCz (Lf-rTMS-FCz; MD -3.25, 95% CrI -6.06 to -0.40), high-frequency deep TMS over FzFC (Hf-dTMS-FzFC; MD -6.48, 95% CrI -12.32 to -0.50), and 2 mA anodal tDCS over F3 with cathodal over Fp2 (MD -9.34, 95% CrI -16.01 to -3.03).For secondary outcomes, high-frequency deep rTMS over FzFCz produced the largest reduction both in depressive symptoms (SMD -1.24, 95% CrI -1.92 to -0.55) and anxiety scores (SMD -1.88, 95% CrI -2.62 to -1.11), but had no effect on Clinical Global Impression-Severity (CGI-S) scores.Specific NIBS protocols are safe and effective adjunctive treatments for OCD, with promising yet inconclusive improvements in comorbid depressive symptoms. Further high-quality, head-to-head trials are needed.

Humans↗

PubMed2026

The impact of body mass index classification on operative characteristics and perioperative outcomes in lumbar microdiscectomy.

INTRODUCTION: Body mass index (BMI) stratification helps classify obesity severity. In patients undergoing microdiscectomy for symptomatic lumbar disc herniation, the effect of obesity on perioperative risk remains incompletely understood. This retrospective single-institution study evaluated whether BMI class influences perioperative risk in a large surgical cohort. METHODS: Adults older than 18 years who underwent primary, elective single-level lumbar microdiscectomy between June 2018 and March 2025 with at least 3 months of follow-up were included. Patients were grouped by BMI: without obesity (WO, BMI < 30), class I (CI, 30-34.9), class II (CII, 35-39.9), and class III (CIII, ≥40). Outcomes were analyzed separately for open microdiscectomy (OM), tubular microdiscectomy (TM), and endoscopic discectomy (ED). Continuous variables were compared using Kruskal-Wallis testing with Dunn post hoc analysis; categorical variables were compared with chi-square tests. Significance was set at p < 0.05. RESULTS: A total of 757 patients were included (OM 422, TM 190, ED 145). Higher obesity classes underwent ED more frequently (p = 0.038). In the OM cohort (WO 258, CI 97, CII 50, CIII 17), CI had a higher proportion of males and CII a lower proportion (p = 0.007). Operative time, length of stay, and estimated blood loss were greatest in CII and CIII patients (all p < 0.001). CII patients also had more emergency department visits within 1 year than other classes (p = 0.026). No differences were found in age, smoking status, disc herniation type, dural tears, intraoperative or postoperative complications, or revision presence/time. In the TM cohort (WO 117, CI 47, CII 21, CIII 5), WO patients were oldest and CIII youngest (p < 0.001), with no other significant differences. In the ED cohort (WO 79, CI 31, CII 20, CIII 15), WO patients were oldest and CIII youngest (p = 0.004). CIII patients had higher estimated blood loss (p = 0.028) and shorter time to revision (p < 0.001), while other variables were similar. CONCLUSIONS: ED was used more often in higher obesity classes. In OM, CII and CIII obesity were associated with longer operative time, longer hospital stay, and greater blood loss, likely due to increased exposure requirements. TM and ED showed few obesity-related differences in complications, suggesting minimally invasive approaches may mitigate obesity-related perioperative risk. However, the retrospective design and small number of CIII patients warrant further study.

Humans↗

PubMed2026

Analysis of deep learning techniques in computer-aided diagnosis for meniscus injuries: a systematic literature review.

Meniscus informatics is a growing subject of study in the healthcare industry. One of the major hindrances to the healthcare system's transformation is obtaining knowledge and meaningful information from complicated, high-dimensional and diverse sources. Modern biomedical research, for instance, has seen an increase in the use of complex, dissimilar, poorly documented, and generally unstructured electronic health records, imaging, sensor data and text, even after many current techniques have been used to extract more robust and useful elements from the data for analysis. New efficient standards for building end-to-end learning models from complex data are therefore needed. Therefore, the current study aims to examine the most recent research on the use of deep learning techniques for diagnosing meniscus tears and recommend creating comprehensive and meaningful interpretable structures that might benefit the healthcare industry. We also draw attention to shortcomings and the need for better technique development, and we provide new perspectives about this exciting new development in the field.

Humans↗

PubMed2026

Data-centric, robust, and explainable multimodal deep learning for clinical decision support: A systematic review.

PURPOSE: Multimodal deep learning is increasingly proposed for clinical decision support (CDS) under a "data-centric" framing that prioritizes label quality, missing-modality robustness, distribution shift, calibration, and explainability. Prior reviews have examined multimodal medical AI, CDS, and data-centric methods separately, but none address their intersection. We mapped the modalities, fusion strategies, and data-centric and explainability techniques used in this recent literature, quantified how often each is implemented rather than merely mentioned, assessed deployment-relevant evidence (external validation, clinical-outcome measurement, equity), and formally appraised study-level risk of bias. METHODS: Following the PRISMA 2020 statement (PROSPERO CRD420261427815; registered retrospectively), we screened 150 records and included primary, clinical, multimodal studies that applied machine or deep learning to a decision-support task and reported at least one quantitative result. Two reviewers screened and extracted data with consensus adjudication. Each study was coded against pre-specified operational definitions, separating implemented or empirically evaluated techniques from those only mentioned. Study-level risk of bias was assessed with PROBAST + AI. Synthesis was narrative. RESULTS: Thirty-one studies met inclusion; 30 (97%) were published between 2024 and 2026, with a median of three modalities (range 2-6), most commonly structured EHR (71%) and imaging (39%). Data-centric techniques were frequently reported (74-84% across label-noise, distribution-shift, calibration, missing-modality and class-imbalance handling; equity 61%). However, external validation was reported in only 4/31 studies (13%), a clinical or provider outcome in 3/31 (10%), and no study reported routine deployment. Overall risk of bias was high in 27/31 studies (87%), driven by the analysis domain. CONCLUSION: Within this recent, self-selected slice of the field, technical robustness and explainability techniques are widely reported but rarely validated out-of-distribution or against clinical outcomes, and the underlying evidence is at high risk of bias. Progress requires external multi-site validation, clinical-outcome measurement, formal bias appraisal, and adherence to AI reporting standards (e.g., TRIPOD + AI) before deployment can be justified.

Deep Learning↗

PubMed2026

Artificial intelligence for dental caries detection: An umbrella review.

Artificial intelligence (AI) has been proposed as a tool to improve dental caries detection across imaging modalities; however, its clinical value remains uncertain. This umbrella review aimed to synthesize and critically appraise systematic reviews evaluating AI for caries detection and diagnosis. An umbrella review was conducted following PRIOR guidance (PROSPERO CRD420261340728). Searches were performed in MEDLINE, Embase, Scopus, Web of Science, and Google Scholar up to 15 March 2026. Methodological quality was assessed using AMSTAR 2, and overlap of primary studies was quantified using the corrected covered area (CCA). Seventeen systematic reviews were included, of which five reported diagnostic test accuracy meta-analyses using bivariate or HSROC models. Across these meta-analyses, pooled sensitivity ranged from 0.76 to 0.94 and specificity from 0.85 to 0.91. Most systems were based on deep learning models applied to bitewing radiographs and intraoral photographs. However, substantial heterogeneity was observed in imaging modalities, lesion thresholds, analytical tasks, and evaluation metrics. In addition, a high degree of overlap across reviews and recurrent methodological limitations, including reliance on retrospective datasets, limited external validation, and inconsistent reporting, substantially weaken the reliability of the evidence. Although AI models demonstrate high diagnostic performance under experimental conditions, current evidence does not support their use as stand-alone diagnostic tools. Their clinical applicability remains limited, and implementation should be restricted to decision-support contexts until robust prospective validation demonstrates meaningful impact on clinical decision-making and patient outcomes.

Dental Caries↗

PubMed2026

Diversity and population connectivity of members of the family Eunicidae inhabiting deep-water corals in the North Atlantic.

Eunicid polychaetes are often found in association with Cold Water Corals (CWCs), even establishing symbiotic relationships, such as those described between Desmophyllum pertusum and Eunice norvegica. While genetic connectivity of CWCs across the North Atlantic has been widely studied, little is known about their associated fauna in this regard. Here, we present a study combining a focused analysis of the genetic and genomic connectivity of E. norvegica with a regional assessment of the distribution and evolutionary relationships of three CWC-associated eunicid species from the Cantabrian Sea and the North of the United Kingdom (190-1,230 m depth). An integrative approach using genetic (16S, COI and 18S), morphological and ecological data allowed the identification of the eunicids studied, with new records of Eunice cf. nicidioformis and Leodice cf. antarctica in the Cantabrian Sea, as well as previously undocumented associations with CWC species. In addition, RADseq data contributed to the delimitation of the closely related species E. norvegica and Eunice philocorallia. Moreover, the genetic connectivity of E. norvegica was studied trough a RADseq (1,067 neutral SNPs) approach. Our results indicate a single panmictic population across approximately 2,000 km, suggesting that oceanographic currents facilitate passive dispersal of E. norvegica lecithotrophic larvae, aided by coral host stepping-stones. The connectivity patterns observed for E. norvegica mirror those of D. pertusum, on which the worm is ecologically dependent. Our study highlights the importance of using integrated genetic, morphological and ecological data to characterise and delineate understudied CWC-associated species and improve our understanding of their dispersal capabilities and genetic connectivity to inform future conservation recommendations.

Animals↗

PubMed2026

Integrating genomic distance analyses in the description of a new family, genus, and species of sponge-associated antipatharians (black corals).

Antipatharians (black corals) are among the least studied coral groups, with much of their diversity still undescribed. Here, we present an integrative morphological, phylogenomic and genomic distance study of deep-sea antipatharians sampled in high seas areas of the North Pacific Ocean and from New Zealand's Exclusive Economic Zone. These corals grow on hexactinellid sponges - a unique characteristic in the order Antipatharia. Using a dataset of ultra-conserved elements and exons, combined with morphological analyses, we reconstruct phylogenomic relationships and formally describe a new family (Eidikopathidae fam. nov.), a new genus (Eidikopathesgen. nov.), and two new species (E. korallispongiasp. nov., E. zealandkoralliasp. nov.). Morphologically, the new family is distinguished by a corallum consisting of a network of loose branches that fuse with the sponge skeletal framework. Phylogenomic analyses recovered consistent topologies with strong nodal support, corroborating the distinct evolutionary placement of this sponge-associated lineage. Pairwise genomic distances estimated using the Tamura-Nei model were concordant with patristic genomic distances, identifying Pteridopathidae as the genetically closest family to Eidikopathidae fam. nov., followed by Myriopathidae and Stylopathidae, which were recovered as sister families in the phylogeny. This pattern shows that genomic distance complements, rather than simply mirrors, tree topology by quantifying accumulated sequence divergence among lineages. Together, these results provide the first genomic distance framework for Antipatharia, offering a baseline for future systematic, evolutionary, and biodiversity studies on this fundamental shallow, mesophotic and deep-sea coral group.

Animals↗

PubMed2026

Artificial intelligence for anticancer drug discovery from natural products of macroalgae and sponges: A systematic review.

Marine natural products (MNPs) from macroalgae and marine sponges have inspired clinically important anticancer agents, including the cytarabine pharmacophore and the eribulin scaffold, while cyanobacterial dolastatin chemistry supplies the auristatin payloads of several marine-inspired antibody-drug conjugates (ADCs) such as brentuximab vedotin. Artificial intelligence (AI) methods, encompassing both classical machine learning (ML) with hand-engineered features and modern deep learning (DL) with many-layered neural networks, are increasingly supporting key decisions in natural-product anticancer drug discovery, including bioactivity prediction, target identification, absorption, distribution, metabolism, excretion and toxicity (ADMET) filtering, generative analogue design, and the selection of preclinical candidates. DL architectures relevant to this field include graph neural networks, transformer-based molecular generators, diffusion models for protein-ligand docking, and convolutional networks for mass spectrometry, while classical ML contributes interpretable fingerprint-based bioactivity models and molecular networking for dereplication. This review follows a systematic literature review methodology to organize the landscape of AI methods now applied to MNP anticancer discovery, distinguishing ML and DL approaches where relevant, situating them within the chemical context of macroalgal and sponge-derived oncology leads, and critically examining published case studies, including validation level (computational, in vitro, in vivo, clinical). The principal bottleneck for medical translation has shifted partly from algorithmic capability toward data infrastructure and experimental validation. Sparse, heterogeneous, and taxonomically biased bioactivity records limit what current models can learn and reduce the reliability of AI-prioritized candidates entering the preclinical pipeline. A roadmap is proposed that prioritizes open MNP-specific benchmarks, symbiont-aware modeling, and active learning loops with synthesizability and ADMET constraints. These AI workflows may accelerate the prioritization of marine-derived anticancer leads and support earlier, more evidence-based translational decisions in oncology drug development.

Biological Products↗

PubMed2026

Bioprospecting microbial genomes to expand the biocatalytic toolbox of rubber oxygenases.

A set of rubber oxygenases was discovered through phylogenetic analysis and AI-based structural modeling of complexes of the putative enzymes with a substrate mimicking cis-1,4-polyisoprene. Sixteen candidate proteins were selected from thermophilic microorganisms, all sequence-related to the Latex clearing protein from Streptomyces sp. K30 (LcpK30). Sequence truncation and solubility tags were then evaluated to enhance protein expression, with the SUMO tag proving to be the most effective. Including LcpK30, nine heme-containing oxygenases were successfully expressed in E. coli NEB 10-beta cells, purified (35-157 mg L-1 yield) and characterized. Steady-state kinetics revealed significant rubber latex-degrading properties for six of them, with the truncated SUMO-fused LcpK30 (SUMO-LcpK30T) showing activity in agreement with literature. Notably, the catalytic efficiencies of all the expressed homologs lay within one order of magnitude and the oxygenase from Thermomonospora echinospora was found to be particularly promising in terms of activity, especially at high latex concentrations (more than 1% w/v). The analysis of reaction mixtures by both HPLC and HPLC-MS confirmed the oxidation of cis-1,4-polyisoprene to form the expected isoprenoid oligomers (n = 2-12), whose distribution was consistent with the usual endo-type cleavage pattern in all but one case. This bioprospecting effort afforded a platform of new rubber-degrading enzymes with diverse efficiencies and product profiles, capable of adapting to targeted applications.

Oxygenases↗

PubMed2026

A systematic review of macaque brain stimulation: Trends and future directions.

Neurostimulation techniques can powerfully modulate neural circuit activity and provide causal insights into the relationship between brain function and behavior. Macaque monkeys have long been a key animal model for brain stimulation studies. While stimulating the macaque brain with one or a few electrodes has already taught us much about brain function and dysfunction, recent technological advances promise a future with more precise stimulation using many more electrodes. However, such possibilities also increase the number of choices an experimenter has when designing their study. We can learn from a rich past, but a comprehensive overview of which brain regions have been studied and with what stimulation parameters is lacking. Here, we present a PRISMA-compliant systematic review of 734 macaque brain stimulation studies using electrical and/or optogenetic stimulation. We find a striking bias in which brain areas have traditionally been stimulated: a mere 10 brain regions account for half of all studies, with the remainder of studies investigating approximately 150 other areas. Across studies, stimulation frequency robustly predicted direct behavioral effects independent of brain region, while amplitude did not. Future studies could more systematically explore less studied regions through lower stimulation frequencies (e.g., 20-50 Hz) alongside established ranges (∼200 Hz). Tools such as fMRI or optical imaging can capture neural circuit engagement evoked by these frequencies, even when behavioral effects are absent or remain subtle. Our synthesis offers a guide towards the next steps in high-channel-count, high-precision stimulation approaches.

Animals↗

PubMed2025

Enhanced fracture detection on radiographs with AI assistance for clinicians: a systematic review and meta-analysis.

BACKGROUND: Emergency radiographic interpretation for fractures is prone to missed or misdiagnoses. Artificial intelligence (AI) is expected to become a powerful tool to assist clinicians in fracture detection. PURPOSE: A systematic review and meta-analysis was performed to assess whether AI improves clinicians' ability to detect fractures on radiographs. MATERIALS AND METHODS: A literature search was conducted in PubMed, Web of Science, and Cochrane Library for studies published between January 1, 2010, and October 10, 2025. A meta-analysis of diagnostic accuracy studies was performed using a Summary Receiver Operating Characteristic (SROC) curve. The quality of included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. Subgroup analysis and meta-regression were conducted to explore potential sources of heterogeneity. RESULTS: A total of 26 studies were included . The pooled sensitivity of clinicians increased from 77% (95% CI: 72-81) to 87% (95% CI: 83-90) with AI assistance, while the pooled specificity improved from 88% (95% CI: 85-90) to 92% (95% CI: 89-94). The corresponding AUC values were 0.90 (95% CI: 0.87-0.92) before and 0.95 (95% CI: 0.93-0.97) after AI assistance. Eight studies were rated as high risk of bias. Subgroup analysis and meta-regression identified potential sources of heterogeneity, including fracture location, AI model type, high risk of bias, and reference standards. CONCLUSION: AI assistance significantly improves clinicians' diagnostic performance in detecting fractures on radiographs for extremity and trunk fractures.

Humans↗

PubMed2026

Applications of quantum AI in brain disorder diagnosis: A systematic review.

BACKGROUND AND OBJECTIVE: Brain disorder diagnosis and prediction remain challenging because neuroimaging, electrophysiological, behavioral, and multimodal data are high-dimensional, noisy, heterogeneous, and limited by small clinical cohorts. This systematic review synthesised applications of quantum artificial intelligence (QAI) for brain disorder diagnosis, prediction, detection, and monitoring. METHODS: Following PRISMA guidelines, studies published from 2016 to 13 January 2026 were retrieved from Scopus, Web of Science, and IEEE Xplore. After screening, 36 studies met the eligibility criteria and were qualitatively analysed according to disorder category, data modality, QAI method, implementation setting, validation strategy, and performance. RESULTS: At the broader disease-group level, neurodegenerative disorders were the most frequently investigated, followed by mental health and psychiatric disorders. At the individual level, Parkinson's disease and schizophrenia were the leading applications, followed by depression, anxiety, Alzheimer's disease, and stress-related tasks. MRI-based modalities were the most frequently used data source, followed by multimodal data and EEG. Methodologically, primary QAI approaches were dominated by quantum neural and QDL architectures, followed by quantum-inspired optimization or feature-selection methods and quantum-kernel/conventional QML classifiers. Qiskit/IBM Quantum and PennyLane were the most frequently reported quantum software frameworks. However, most studies relied on simulators, classical quantum-inspired implementations, or unclear implementation settings, with limited real-hardware evaluation. CONCLUSIONS: QAI shows emerging potential for brain disorder analysis, particularly through hybrid quantum-classical learning, quantum neural architectures, quantum-kernel methods, and quantum-inspired optimization. Nevertheless, current evidence remains preliminary and requires larger datasets, subject-level and external validation, fair classical benchmarking, noise-resilient circuits, real quantum hardware evaluation, explainability, and clinical validation.

Humans↗

PubMed2026

Comparing the efficacy of chlorhexidine and povidone-iodine for surgical site disinfection: a systematic review and meta-analysis from randomized controlled trials.

BACKGROUND: Randomized controlled trials report conflicting evidence on the efficacy of different skin disinfectants for preventing surgical site infection (SSI). METHODS: We systematically searched PubMed, Web of Science, Cochrane Library, and Embase for RCTs published up to February 2025 comparing preoperative skin disinfection with povidone-iodine (PVI) versus chlorhexidine (CH). Primary outcomes were overall, superficial, deep, and organ/space SSI rates. Secondary outcomes included hospital stay, readmission, and reoperation. RESULTS: CH was superior to PVI in preventing overall SSI (26 studies, n = 29,356; RR: 0.89; 95% confidence interval [CI]: 0.80 to 0.99). The overall SSI incidence rate in the CH group was 7.1% (1,045/14,677), compared with 7.8% (1,152/14,679) in the PVI group, equating to an 11% reduction in relative risk and a 0.7% reduction in absolute risk. The number needed to treat to prevent one SSI was 143. CH demonstrated superiority over PVI in preventing superficial SSI (13 studies, n = 16,867; RR: 0.77; 95% CI: 0.64 to 0.92), but not for deep SSI (11 studies, n = 15,842; RR: 1.00; 95% CI: 0.77 to 1.29) or organ SSI (9 studies, n = 9,471; RR: 1.17; 95% CI: 0.89 to 1.53). No significant differences were found in hospital stay, readmission, or reoperation rates between the two groups. CONCLUSION: CH demonstrates statistical superiority over PVI in preventing overall and superficial SSI, though the absolute clinical benefit is modest. No significant differences were observed for deep or organ/space SSI, nor for secondary outcomes including hospital length of stay, readmission, or reoperation rates.

Humans↗

PubMed2026

Using Organoids to Unlock the Potential of Human Torpor for Spaceflight.

PURPOSE OF REVIEW: This paper reviews the current understanding of the potential for humans to enter a state of torpor/hibernation, and discusses the possibility of inducing torpor in astronauts for long-duration space travel, including some of the physiological, technological, and ethical considerations associated with its implementation. By exploring means to induce torpor in various human organoid systems, we hope such research can provides insights to comprehensive solutions to overcome some of the major hurdles that limit the potential for human to enter a state of torpor during long-duration deep-space missions, and contribute to the ongoing efforts to make such missions more feasible and safer for astronauts. RECENT FINDINGS: On future deep space missions such as NASA's planned missions to the Moon, Mars, and near-Earth asteroids, astronauts will be continuously exposed to environments that are radically different from those on Earth, each presenting multiple logistical and physiological challenges. Beyond the well-documented physiological effects of microgravity, space travelers will encounter a complex radiation environment that may contribute to significant short- and long-term adverse effects on human physiology and increase the risk of cancer and other diseases. Besides these physical challenges, life support systems must also be designed to mitigate psychological impacts of long-term isolation and confinement - all of which collectively pose formidable engineering problems. Hibernation/torpor is a state of prolonged inactivity and metabolic depression used by a wide variety of mammals to survive periods of cold temperatures and food scarcity, including some primates and perhaps even an extinct early line of hominins that lived nearly half a million years ago. Since modern humans share common ancestry with these hominins and hibernating primates, it is likely the human genome encodes the necessary genetic information to hibernate, or at least enter the similar, more transient state of torpor. The reduced body activity, lowered metabolism, and decreased energy requirements that characterize torpor suggest that developing means of inducing such a state in astronauts could address these challenges, including providing a degree of radioprotection. SUMMARY: This review explores the potential application of human torpor as a countermeasure to address the many challenges posed by long-duration spaceflight beyond low-Earth orbit (LEO), discusses various natural hibernating model systems for studying means of inducing a torpor-like state in humans, and highlights the vast potential of using human organoids to test and validate mechanisms that govern induction and maintenance of torpor to identify the means to one day safely induce this state in astronauts to provide additional protection from the myriad stressors of spaceflight.

Astronaut Health↗

PubMed2026

Mechanisms linking the gut microbiota to colorectal cancer development and progression.

Colorectal cancer remains a leading cause of global cancer mortality, with a concerning rise in early-onset cases driven by complex interactions between environmental exposures, lifestyle factors, and host genetics. Mounting evidence indicates that gut microbiota dysbiosis critically modulates this oncogenic process, acting as an active participant rather than a passive bystander. This review systematically synthesizes the dichotomous roles of the intestinal microbiome in colorectal tumorigenesis through the conceptual framework of the driver-passenger model. We discuss how early initiating driver bacteria, such as Polyketide synthase-positive Escherichia coli and enterotoxigenic Bacteroides fragilis, compromise mucosal barriers, induce chronic mucosal inflammation, and inflict direct genomic instability. As the local tumor microenvironment undergoes profound metabolic remodeling, opportunistic passenger pathogens, notably Fusobacterium nucleatum, become enriched, further promoting cellular proliferation and facilitating tumor immune evasion. Conversely, protective commensals, exemplified by Clostridium butyricum and Streptococcus thermophilus, exert robust tumor-suppressive effects through multifaceted mechanisms. These beneficial microbes actively antagonize malignant progression by redirecting tumor metabolic fluxes toward oxidative stress, orchestrating deep epigenetic reprogramming, and degrading core oncoproteins to reverse chemoresistance. Transitioning from fundamental mechanisms to clinical application, we evaluate a comprehensive spectrum of microbiota-targeted interventions, encompassing non-invasive diagnostic biomarkers, fecal microbiota transplantation, engineered bacteria, phage therapy, and postbiotics. Finally, we critically address the formidable translational challenges associated with microbial heterogeneity, long-term safety, and regulatory standardization, aiming to provide a balanced perspective on integrating microbiome-based strategies into next-generation precision oncology for colorectal cancer.

Humans↗

PubMed2026

Clinical insights into catathrenia: A real-world analysis from a tertiary sleep center.

INTRODUCTION: Catathrenia is a rare sleep-related breathing disorder marked by groaning during prolonged expiration, often underrecognized or misdiagnosed as obstructive or central sleep apnoea (OSA or CSA) or parasomnia. Understanding its clinical and polysomnographic features is essential for accurate diagnosis and management. MATERIALS AND METHODS: We performed a retrospective observational study of adult patients diagnosed with catathrenia at Serviço de Medicina do Sono de Coimbra. Diagnosis was established by attended overnight polysomnography (PSG) with synchronised audio-video recording. Demographic data, symptoms, comorbidities, PSG variables, treatment modalities, and outcomes were reviewed. Catathrenia events were defined as deep inhalation followed by prolonged exhalation with monotonous groaning. RESULTS: Ten patients were included. Median age was 46 years (range 27-78), mostly female (70%). Common comorbidities included obesity (n = 4), depression (n = 2), Parkinson's disease (n = 1), and restless legs syndrome (n = 1). Six patients (60%) had concomitant obstructive sleep apnoea (OSA). Seven patients had excessive daytime sleepiness (Epworth Sleepiness Scale > 10). All catathrenia episodes occurred exclusively during REM sleep. Continuous positive airway pressure (CPAP) therapy was the most frequently used treatment and was associated with objective or subjective improvement in most patients. Two patients experienced spontaneous remission. CONCLUSION: Catathrenia remains underdiagnosed and can mimic other sleep disorders. Recognition of its REM-sleep predominance and PSG pattern is essential. Individualised treatment, often involving PAP therapy, may improve symptoms and patient outcomes.

Humans↗

PubMed2026

Cytonuclear conflict and reticulate evolution in the Morelloid clade (Solanum, Solanaceae): Insights from genome skimming and network Phylogenomics.

The Morelloid clade (black nightshades) is one of the most strongly supported clades within the megadiverse Solanum genus. It comprises 76 globally distributed, non-spiny herbaceous and suffrutescent species. While often erroneously considered poisonous weeds, several species are economically important as orphan crops. The clade is closely related to tomato and potato but, due to a lack of focused breeding efforts, remains a putative reservoir of genetic diversity for crop improvement. Despite this potential, we lack fundamental knowledge on the evolution of the Morelloid clade. The group includes polyploid species with unknown parental origins-likely reflecting reticulate processes such as hybridization, introgression, and associated backcrossing events. Prior analyses have been unable to disentangle these processes, leaving the mechanisms underlying reticulate evolution in the Morelloid clade poorly understood. Here, we use genome skimming to produce a well-supported maximum likelihood plastid phylogeny from complete circularized plastomes and a coalescent-based species tree from combined Angiosperms353 and conserved ortholog set nuclear markers. Our dataset, composed of previously published data and deep genome skimming from herbarium samples, spans 26 Morelloid species. To investigate phylogenetic discordance, we used a nuclear phylogenetic network, multispecies coalescent simulations, a fused rooted nuclear chloroplast tree, and quantification of nuclear gene tree concordance. We show that incongruence between nuclear and plastid trees is pervasive and cannot be explained by incomplete lineage sorting alone. Instead, our results demonstrate that events consistent with repeated chloroplast capture have shaped the reticulate evolutionary history of the clade, especially among African polyploid and Pan-American diploid lineages.

Phylogeny↗