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Locust phase polyphenism is a remarkable example of phenotypic plasticity, driven by population density to produce a dramatic shift between cryptic, solitarious and swarming, gregarious phenotypes. Despite over a century of research, the evidence base lacks systematic synthesis. We conducted a systematic review of 400 studies on locust phase polyphenism, integrating evidence across ecological, neurobiological, physiological, molecular, epigenetic, and microbial drivers. The results revealed that the evidence base is constrained by two critical limitations. First, severe taxonomic narrowness: 93.8% of studies focus on at least one of two model species (desert locust, Schistocerca gregaria and migratory locust, Locusta migratoria), with only 6.2% examining other locust species exclusively. Second, profound methodological disconnect: 84.5% of studies are laboratory-based, while field-only (6.0%) and integrated field-laboratory studies (6.2%) together constitute only 12.2% of the literature. Within this paradigm, mechanistic research has successfully mapped proximate pathways from tactile stimulation and serotonin/dopamine signaling to transcriptomic reprogramming and epigenetic regulation. However, direct species comparisons reveal fundamental divergence rather than conservation, challenging assumptions of universal mechanisms. Laboratory-derived pathways remain poorly integrated with field ecology-vegetation structure, nutritional geography, and climate dynamics-creating a translational impasse for predictive management. Emerging areas such as microbiome dynamics and transgenerational epigenetics require causal validation under ecologically relevant conditions. Reliance on the current narrow paradigm fundamentally limits both biological understanding and practical application. We propose a future research prioritizing: (1) phylogenetically broad comparative multi-omics to distinguish conserved cores from lineage-specific adaptations; (2) integrated field-laboratory experiments incorporating climate and landscape heterogeneity; (3) causal validation of emerging regulators in ecologically relevant contexts; and (4) translation of comparative insights into species-specific management tools through equitable partnerships with researchers and practitioners in outbreak-affected regions. Such integration is essential for developing predictive, sustainable management strategies in an era of global change.

Obstructive sleep apnea (OSA) is associated with excess mortality, and readily obtainable biomarkers may support pragmatic risk stratification in OSA, but their comparative and incremental value remains uncertain. We benchmarked inflammation-nutrition indices and TyG-related metabolic indices for mortality risk in adults with questionnaire-defined OSA and evaluated whether these markers improve 5-year all-cause mortality prediction beyond a basic clinical model. We analysed 3,503 adults with questionnaire-defined OSA from the NHANES 2005-2008 and 2015-2018 derivation cohorts. Seven candidate biomarkers were evaluated: TyG, TyG-BMI, TyG-WC, TyG-WHtR, TG/HDL-C, advanced lung cancer inflammation index (ALI), and neutrophil percentage-to-albumin ratio (NPAR). Survey-weighted Cox models and restricted cubic splines were used to characterise mortality associations. Two 5-year all-cause mortality prediction strategies were developed: a tertile-based model (Model 1) and a continuous-scale model (Model 2). Internal validation used bootstrap optimism correction, calibration curves, Brier scores, and decision-curve analysis. Incremental value beyond a prespecified base model was assessed using likelihood-ratio testing, change in AUC, and net reclassification improvement (NRI). External validation was performed in an independent multicenter Chinese cohort of 200 patients from six hospitals. All included patients had at least 5&#x2009;years of observation time from baseline assessment, allowing complete ascertainment of the binary 5-year all-cause mortality endpoint. The externally validated object was the final Base&#x2009;+&#x2009;Combine model, and its performance was assessed alongside the base model using ROC analysis, calibration plots, calibration intercept and slope, Brier score, and decision-curve analysis. During a median follow-up of 57.0&#x2009;months (IQR 33.0-150.0), 293 all-cause deaths occurred in the derivation cohort. Among individual biomarkers, ALI showed the most robust mortality-related signal across analyses, whereas NPAR showed signal in selected single-marker and nonlinear analyses but was less consistent after full multivariable adjustment. TyG-related indices were also variably associated with mortality and contributed mainly within the combined prediction model. In Model 1, the full 7-marker composite model achieved an AUC of 0.735, a bootstrap-corrected AUC of 0.721, and a Brier score of 0.039. In Model 2, the best-performing combined model incorporated TyG-BMI, TyG-WC, TyG-WHtR, TG/HDL-C, and ALI, yielding an AUC of 0.765, a bootstrap-corrected AUC of 0.761, and a Brier score of 0.038. Internal calibration was acceptable for both derivation models, with Model 2 performing better. Decision-curve analysis showed positive net benefit over treat-all and treat-none strategies, with a wider clinically useful threshold range for Model 2. Compared with the base model, the combined biomarker model improved model fit (likelihood-ratio test p&#x2009;<&#x2009;0.001) and reclassification (NRI p&#x2009;<&#x2009;0.001), although the increase in AUC was modest. In the external validation cohort, the prespecified final Base&#x2009;+&#x2009;Combine model achieved an AUC of 0.697 (95% CI 0.581-0.813), compared with 0.672 (95% CI 0.553-0.791) for the base model. External calibration remained imperfect for both models. The base model showed a calibration intercept of -1.609, a calibration slope of 0.385, and a Brier score of 0.138, whereas the final Base&#x2009;+&#x2009;Combine model showed a calibration intercept of 3.782, a calibration slope of 0.402, and a Brier score of 0.089. In external decision-curve analysis, the base model provided greater net benefit at lower threshold probabilities, whereas the final Base&#x2009;+&#x2009;Combine model showed greater net benefit mainly within a narrower higher-threshold range (approximately 0.14-0.30). Given the limited number of external events, these findings should be interpreted as preliminary evidence of transportability rather than as definitive support for a clearly superior prediction tool. In adults with questionnaire-defined OSA from NHANES, inflammation-nutrition markers, particularly ALI, showed stronger mortality-related signal than TyG-related indices at the single-marker level, while NPAR showed supportive signal in selected analyses. More importantly, a combined inflammatory-metabolic biomarker model provided modest incremental enrichment for 5-year all-cause mortality risk benchmarking beyond routine clinical variables. In an external multicenter cohort with PSG-confirmed OSA, the final Base&#x2009;+&#x2009;Combine model showed moderate but preliminary transportability, with slightly improved discrimination but poor calibration. Any incremental net benefit in external decision-curve analysis appeared to be confined to a relatively narrow higher-threshold range. These findings support pragmatic risk benchmarking rather than immediate use as a clearly superior clinical prediction tool.

The superior mesenteric artery (SMA) syndrome is a rare condition where the third part of the duodenum is compressed between the abdominal aorta and the SMA. It often causes nonspecific upper abdominal symptoms that mimic common problems like gastritis, leading to delays in diagnosis. A 23-year-old woman from central Nepal had 5 years of epigastric pain, nausea, occasional vomiting, acid brash, and gradual weight loss. She was repeatedly treated for gastritis without improvement. Initial ultrasound was normal, and endoscopy showed antral gastritis. Contrast-enhanced CT revealed a very narrow aorto-mesenteric angle (14&#xb0;) and distance (3.6 mm), confirming SMA syndrome, along with the narrowing of the celiac trunk, thrombosis of the SMA, and signs of pelvic congestion. Conservative treatment failed, and she underwent open duodenojejunostomy. After surgery, her symptoms resolved completely, and she gradually gained weight. This case shows that the SMA syndrome can be missed for years, especially when endoscopy shows only secondary gastritis. Early CT scanning and timely surgery can cure symptoms and prevent prolonged suffering. The SMA syndrome should be considered in young patients with chronic upper abdominal pain, persistent vomiting, and unexplained weight loss. Prompt imaging and surgical management are often needed for lasting recovery.

Halide perovskite quantum dots (HPQDs) are transformative candidates for next-generation optoelectronic devices, owing to their exceptional optoelectronic properties including widely tunable bandgaps, ultrahigh color purity, and solution processability. However, scalable, deterministic synthesis of high-quality HPQDs with simultaneous ultra-narrow emission linewidth and high photoluminescence quantum yield (PLQY) remains a longstanding challenge, fundamentally limited by the mass transfer bottleneck and poor mixing efficiency of conventional laminar microreactors. Here, we report a biomimetic vein-inspired ultrasonic microreactor integrated with sharp-edged microstructure arrays to address this core challenge. Through systematic multiphysics simulations, we quantitatively decode the acoustic-hydrodynamic coupling mechanism in the microreactor, and establish a quantitative structure-performance relationship between microstructure geometry and sonochemical reaction performance. We identify an optimized cylindrical microstructure configuration that synergistically amplifies acoustic streaming and cavitation yield to break laminar boundary layer confinement. Experimental validation confirms the optimized microreactor enables continuous synthesis of high-quality HPQDs with an ultra-narrow full width at half maximum of 23.28&#xa0;nm and PLQY up to 78.6%, markedly outperforming conventional microfluidic methods. We further elucidate that cavitation-enhanced micromixing enables dynamic supersaturation tuning, driving LaMer-type size-focusing and homogeneous nucleation for exceptional HPQDs monodispersity. This work provides a generalizable, scalable microfluidic strategy for precision synthesis of high-performance optoelectronic nanomaterials, bridging the critical gap between lab-scale research and industrial translation.

One-dimensional (1D) structures provide a unique platform to study the correlated quantum interactions and phase transitions such as unconventional magnetism and superconducting states. Here, we report that iron chalcogenide K_{3}Fe_{2}Se_{4} exhibits an unusual block-type canted antiferromagnetic (AFM) order with a clear single chain quasi-1D structure, which is structurally different from the two-leg ladder BaFe_{2}Se_{3}, through both experimental measurements and density matrix renormalization group (DMRG) calculations. The narrow bandgap semiconductor K_{3}Fe_{2}Se_{4} has a quasi-1D edge-shared FeSe_{4} tetrahedra chain structure and orders antiferromagnetically below 110&#xa0;K. The magnetic moments couple antiferromagnetically along the quasi-1D chain direction of the b axis and form an up-down-down-up (&#x2191;-&#x2193;-&#x2193;-&#x2191;)-like spin structure with a commensurate propagation vector k=(0,0,0), where block-type spin &#x2191;-&#x2191; or &#x2193;-&#x2193; coupling are between the longer Fe-Fe bonds of the quasi-1D chain. DMRG results show that block antiferromagnetic state is stable in K_{3}Fe_{2}Se_{4} and reveal that the block-ordered arrangement of Fe^{2.5+} ions spins arise from the competition between ferromagnetic and AFM interaction in the presence of strong electronic correlation. Our research results not only report the discovery of a clear block-type canted antiferromagnetic structure in a real quasi-1D chain material but also provide a theoretical approach to understand the block-type antiferromagnetism in quasi-1D iron chalcogenides.

This paper examines how radiating surface structure can influence the properties of beam-shaping of the piezoelectric piston type underwater acoustic transducers. The study is done using a broad theoretical, numerical and experimental method. This study presents a comprehensive analysis of the far-field radiation characteristics of circular, square, hexagonal, and octagonal piston-type ultrasonic transducers for underwater applications. The models were validated with three-dimensional finite element simulations and experimental measurements using a wafer Tonpilz transducer prototype. This analysis demonstrates that piston geometry has no significant effect on radiation characteristics for small apertures whose Equivalent Circular Diameter (ECD) is less than half the wavelength. The circular piston is found to exhibit superior performance at increased apertures. Result shows that circular pistons provide superior beam uniformity, narrow main lobes, and low side-lobe levels, making them highly efficient for focused energy transmission, sonar, and underwater communication systems. Experimental validation is provided only for the circular case, while theoretical and numerical results are presented for all pistons. This control of the beam makes the circular piston the solution to accurate acoustic control. Finite element analysis and experimental measurements on a circular wafer transducer proved the validity of the theoretical models. This high level of agreement validates that the findings can directly applied to designing advanced underwater acoustic arrays.

Pathology vision-language models (VLMs) are promising for building the clinical decision support systems. However, a key barrier to real-world clinical deployment lies in the lack of rigorous and clinically meaningful model evaluation. Existing pathology visual question answering (VQA) benchmarks exhibit the weak alignment of vision-language information, insufficient support for knowledge-intensive reasoning, and a narrow disease spectrum, thereby undermining the ability to provide clinical-level evaluation of model performance. In this work, we introduce OmniPathoVQA, a new pathology VQA benchmark offering a broad disease coverage, spanning all human anatomical systems, and thousands of disease entities. To enhance well-organized visual-language alignment, we leverage pathology educational materials and design a fine-grained extraction pipeline for linking pathology images with the correct knowledge. To examine the knowledge depth for pathological reasoning, the hard-version questions are designed based on microanatomic features underlying similar diseases. A comprehensive evaluation of eighteen VLMs reveals that closed-source VLMs attain higher scores compared to open-source counterparts, yet their performance drops dramatically in difficult questions. We find that strong general reasoning ability is a crucial catalyst for the successful fine-tuning and pathology feature interpretation. Our study establishes a rigorous standard for assessing high-level reasoning and guiding the rapid development of clinical pathology VLMs.

The A-site layer-ordered double perovskites, LnBaFe2O6 with Ln from Pr to Dy and Y, contain mixed and unusually high valent Fe3.5+ ions at high temperatures and upon cooling exhibit successive charge disproportionation transitions to relieve the electronic instability. The first charge disproportionation transition can be represented as 2Fe3.5+ &#x2192; Fe3+ + Fe4+. This first-order transition is accompanied by a drastic change in structural and magnetic properties, signaling a strong entanglement of charge, spin, and lattice degrees of freedom. The second charge disproportionation transition, 2Fe4+ &#x2192; Fe3+ + Fe5+, is a second-order-like transition that occurs via intermediate states of LnBa(Fe3+Fe(4-&#x3b4;)+0.5Fe(4+&#x3b4;)+0.5)O6. The LnBa(Fe3+1.5Fe5+0.5)O6 ground state is finally stabilized at low temperature. The size of the A-site Ln ion strongly influences the structural and magnetic properties of the compounds. As the size of the Ln3+ ion increases, the temperature of the first charge disproportionation transition decreases almost linearly, while the temperature of the second charge disproportionation transition increases. Consequently, the temperature range over which the intermediate Fe4+ or Fe4+-like Fe(4&#xb1;&#x3b4;)+ state is stable narrows. These results demonstrate control over the stability of mixed and unusually high valence ions.

While low-dose computed tomography (LDCT) has significantly reduced lung cancer mortality, its full potential remains constrained under current screening paradigms. This review evaluates clinical discrepancies in LDCT implementation and explores adaptive screening frameworks. We synthesized clinical screening outcomes and investigated tumor natural history to identify the drivers of observed misalignments, providing the rationale for optimized screening strategies. Evidence highlights significant disparities between Western and Eastern cohorts, with East Asian initiatives identifying a much higher proportion of early-stage disease driven by ground-glass nodules (GGNs) in non-smokers. These discrepancies are rooted in the diverse evolutionary trajectories of lung cancer, ranging from indolent GGNs with protracted natural history to aggressive subtypes with narrow "curative time windows". The mismatch between conventional smoking-centric protocols and these biological realities leads to a screening paradox: overscreening, which involves excessive screening frequency beyond clinical necessity primarily among low-risk individuals (e.g., annually) but also occurring within high-risk groups, and the intensive follow-up of indolent or clinically insignificant lesions, coexists with underscreening, which encompasses the failure to reach high-risk populations, the lack of adequate coverage for traditionally low-risk individuals, and the oversight of aggressive early lesions. To provide a conceptual basis for potentially reconciling these misalignments, we explore the "curative time window" theory and a hypothesis-generating "low-age, low-frequency" strategy. This framework considers the rationale for earlier baseline screening to intercept aggressive, early-onset cases while potentially extending intervals for individuals with negative baseline results or indolent trajectories to optimize resource allocation. Current smoking-centric guidelines may lack the flexibility to accommodate the biological diversity and shifting epidemiology of lung cancer. A transition toward risk-adapted screening frameworks, such as the conceptual "low-age, low-frequency" strategy, is suggested as a potential approach to optimize curative outcomes and screening efficiency, though prospective validation remains warranted.

Although immunotherapy has transformed outcomes in several solid tumors, it has yielded little survival benefit in glioblastoma (GBM). This limited efficacy may in part reflect not only modest drug activity and a chronically immunosuppressive microenvironment, but also a temporal mismatch between fixed treatment schedules and a tumor-immune ecosystem that evolves across space and time. This review outlines the GBM Immune-Spatiotemporal Feedback Loop (GBM-ISFL), a clinician-governed, hypothesis-generating framework that conceptualizes adaptive immunotherapy as a process of longitudinal sensing, biologic state inference, phase-matched intervention, and iterative feedback. Drawing on single-cell and spatial multi-omics, radiologic assessment, and liquid-biopsy studies, we outline a four-phase atlas of GBM evolution and define a patient-specific Critical Transition Window. This window may functionally overlap with the post-radiotherapy interval highlighted by Response Assessment in Neuro-Oncology (RANO) 2.0, but it should not be treated as a fixed calendar block or as a validated clinical interval. To narrow the resulting observability gap, we position spatiotemporal graph neural networks (STGNNs) as candidate tools for noninvasive inference of latent tumor-immune states from serial multimodal data, including imaging dynamics, treatment exposure, and circulating biomarker trajectories. We further describe how uncertainty-aware state inference could support exploratory phase-specific therapeutic reasoning, translational validation, and lifecycle governance. By reframing GBM immunotherapy around biologic phase rather than chronology alone, the GBM-ISFL offers a testable route toward adaptive, state-informed, and clinically governed precision intervention, but it should not be interpreted as a current standard-of-care algorithm.

Susceptibility to pulmonary infection depends not only on the patient's clinical condition but also on the characteristics of the microorganism, such as its virulence and transmissibility, which are influenced by environmental factors, antimicrobial resistance patterns, and the emergence of pandemics. Understanding these factors, together with the patterns of pulmonary dissemination and their radiological appearances, helps narrow the differential diagnosis. In the emergency setting, however, the radiologist's role extends beyond suggesting the aetiology of pneumonia. Recognition of potential complications, guidance on treatment implications, and assessment of prognosis are key contributions. This paper illustrates these concepts through a literature review and real clinical cases.

Early-morning worsening of asthma, most often observed between 03:00 and 06:00 am, arises from circadian regulation of airway smooth muscle tone, enhanced nocturnal release of inflammatory mediators, and the physiological nadir of endogenous cortisol levels. During this vulnerable interval, patients experience marked airway narrowing; however, conventional oral and inhaled therapies frequently fail to maintain adequate drug concentrations, highlighting the need for delivery systems capable of chronologically programmed pulsatile release. This review aims to critically evaluate recent advances in compressed-coated chronotherapeutic tablets (CCCTs) for early-morning asthma, with emphasis on polymer selection, hydration swelling-rupture mechanisms, and material characteristics governing lag-time precision, and to explore the interplay between circadian pathophysiology and pharmacokinetic-pharmacodynamic alignment in optimizing pulsatile oral drug delivery. An extensive and systematic literature search was conducted across PubMed, Scopus, Web of Science, and Google Scholar, covering publications from January 2000 to November 2025, using terms including chronotherapy, pulsatile drug delivery, compressed-coated tablets, press-coated tablets, lag-time systems, circadian asthma, and oral chronotherapeutic formulations. Preclinical, clinical, formulation, mechanistic, and translational reports published in English were critically analysed to synthesise advances in formulation design, material engineering, biopharmaceutical performance, and clinical relevance of CCCTs for early-morning asthma. Compressed-coated chronotherapeutic tablets (CCCTs) offer a robust oral pulsatile platform that can be designed to provide a defined lag phase followed by rapid drug release after bedtime administration, with lag-time precision governed predominantly by polymer hydration, swelling, and rupture behaviour. Appropriately timed drug exposure aligned with circadian pathophysiology was found to enhance bronchodilation and anti-inflammatory efficacy while minimizing unnecessary nocturnal exposure. Preclinical findings and emerging clinical evidence supporting circadian-aligned delivery are summarized, together with key translational considerations including biorelevant dissolution testing, in vitro-in vivo correlation, scale-up challenges, and regulatory expectations for pulsatile oral systems. By integrating chronobiology with formulation engineering and quality-by-design principles, CCCTs represent a rational and adaptable strategy for synchronizing oral asthma therapy with endogenous biological rhythms. This review positions CCCT-based chronotherapy as a promising approach to improve early-morning symptom control and underlines the critical challenges that must be addressed to enable successful clinical translation.

Artificial intelligence (AI) tools for digital pathology have crossed the threshold from pilot project to clinical deployment, with regulatory approvals, commercial products and prospective trials now accumulating across multiple tumour types. However, global-scale implementation demands careful evaluation of both opportunities and challenges. Traditional histopathology faces mounting pressures including interobserver variability and escalating workloads. At the same time, molecular diagnostics have become increasingly complex, while core workflows have remained fundamentally unchanged for decades. Digital pathology infrastructure and AI algorithms promise solutions through enhanced diagnostic accuracy, improved efficiency, democratised access to expertise and unprecedented research capabilities. AI has already demonstrated clinical value across multiple subspecialties, including prostate, breast and colorectal cancer, with applications spanning automated biomarker quantification, molecular subtype prediction and prognostic modelling. Systematic reviews also identify important limitations that temper enthusiasm for AI adoption. These include heterogeneity in study design, publication bias favouring positive results, limited generalisability across diverse populations and practice settings, concentration of research in narrow subspecialties and insufficient external validation. Critical implementation barriers include substantial infrastructure requirements, concerns around data privacy and security, gaps in regulatory oversight and the risk that algorithmic bias may exacerbate health disparities. Professional bodies advocate for responsible deployment guided by evidence-based standards. Key priorities include rigorous validation, workforce training, workflow integration and continuous performance monitoring. Making transformation equitable, safe and effective requires balancing innovation with accountability. AI should augment rather than replace pathologist expertise and be supported by robust governance frameworks that prioritise patient safety and diagnostic quality over commercial consideration.

Amphiphilic copolymers have emerged as powerful, detergent-free tools for solubilizing biological membranes, enabling the extraction and stabilization of membrane proteins within native-like lipid environments. We report a comprehensive, multi-technique elucidation of how polymer:lipid stoichiometry governs the formation, size, and stability of styrene-maleic acid lipid particles (SMALPs). Using commercial SMA2000, a styrene-maleic acid copolymer made using free radical polymerization and 1,2-ditetradecanoyl-sn-glycero-3-phosphocholine (DMPC) as a model phospholipid, we prepared SMALPs across a wide range of polymer-to-lipid weight ratios and employed an integrated suite of orthogonal characterization methods, including size exclusion chromatography (SEC), dynamic light scattering (DLS), flow-induced dispersion analysis (FIDA), mass photometry, ensemble and time-resolved F&#xf6;rster resonance energy transfer (FRET), and small-angle X-ray scattering (SAXS), to establish the structural consequences of varying polymer content. Our data reveal that efficient lipid solubilization into nanodiscs requires a minimum amount of polymer. Above &#x223c;1% (w/v) SMA2000: 1% DMPC, well-defined nanodiscs of &#x223c;10&#xa0;nm diameter are formed that, on average, exhibit a consistent stoichiometry of &#x223c;130 lipids encircled by &#x223c;11 polymer chains. Through a new molecularly-constrained SAXS model, we show that these nanodiscs possess a stable bilayer height across variations in polymer to lipid ratio and a narrow polymer belt, and that their structural parameters remain invariant once excess polymer is used. In contrast, insufficient polymer (<1% w/v) generates bimodal populations including substantially larger discs. Notably, nanodiscs formed at optimal polymer:lipid ratios remain structurally stable for at least two months. Together, these results provide a rigorous quantification of SMALP composition and preferred size, enhancing our understanding of polymer-lipid nanodisc formation and offering critical design rules for detergent-free membrane protein extraction.

Acinetobacter baumannii is a critical-priority pathogen with increasing antibiotic resistance. Here, we define the mechanism of abaucin, a first-in-class narrow-spectrum antibiotic that selectively targets A. baumannii by inhibiting its essential lipoprotein transporter, LolDF. Extending prior studies of archived strains, we demonstrate potent activity against clinically isolated carbapenem-resistant A. baumannii (CRAB) strains both in vitro and in a murine pneumonia model. Cryo-EM structures of abaucin-bound LolDF reveal symmetric binding of two abaucin molecules within the LolDF cavity, which lock the transporter in a non-productive, outward-open conformation. Biochemical and structural analyses show that abaucin does not block substrate binding but instead traps the substrate-loaded transporter and prevents transfer to LolA. Together, these findings uncover a unique symmetry-enabled conformation-hijacking mechanism and establish LolDF as a tractable target for precision antibiotic development.

Evolution of drug-resistant mycobacterial infections warrants renewed efforts in identifying more efficient preventive and control strategies as well as alternative treatment options. Interest in phage therapy is regaining significant traction, especially in cases of failed conventional therapy. However, phage therapy faces challenges, including the identification of a suitable therapeutic phage, phage delivery, phage resistance, and host immunity. This article reviews existing clinical literature on the therapeutic use of mycobacteriophages as adjuncts to antibiotics in the treatment of drug-resistant mycobacterial infections and discusses such aspects as mycobacterial phage resistance, coevolutionary phage training, and impact of host immunity, as well as the benefits and limitations of phage therapy. To date, at least 27 patients received mycobacteriophage therapy, where M. abscessus accounts for 82.1% of all cases in contrast to M. chelonae (7.1%), M. avium complex (3.6%), and BCG (3.6%). Mycobacteriophages used were either wild-type or derivatives of BPs, D29, Fionnbharth, Fred313, Itos, Muddy, or ZoeJ. Evolution of phage resistance is rare, and the impact of host immunity varies between patients, with most treatment outcomes having little to do with immune responses. However, identification of a suitable therapeutic mycobacteriophage remains a pressing challenge, especially for infections involving the smooth morphotype of M. abscessus. Only about 10 mycobacteriophages made it to clinical use, including wild-type, host range mutants and engineered derivatives, which warrants the expansion of this narrow arsenal of therapeutic mycobacteriophages by building novel candidates or expanding the host ranges of existing ones. The outcomes recorded in these case reports represent a significant achievement. However, the results remain exploratory due to sample size limitations and therefore warrant larger, methodologically rigorous, controlled trials in the future.