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Government-led repurposing programmes are reshaping the division of labour in pharmaceutical innovation. A new power drafted into the European Union pharmaceutical reform package will allow the European Medicines Agency (EMA) to add new therapeutic indications to marketed medicines without the marketing authorization holder's consent. Companies oppose this power, but in weighing up enacting the power, society has a poor understanding of its potential to help patients. This study offers the first empirical assessment of the promise of the power. It analyses 198 medicines from 12 years, comparing EMA-authorized labels with those authorized by the US Food and Drug Administration and a leading reference for off-label uses. Sixty-seven per cent of the medicines have at least one additional use supported by clinical evidence, yielding 320 potential new uses. Of these, 39 per cent are for new diseases and 61 per cent for new patient cohorts, a third of the latter concerning paediatric populations. Commentators generally omit discussing repurposing for new patient cohorts, even though it is a focus of the European Commission. The study's results suggest that the power could be used to authorize a meaningful number of evidence-based uses, especially those already authorized in the USA, while also revealing a policy synergy for neglected populations.

Skin is the largest organ of the body, which comprises of 16 % of the body weight. It has layered structure which consists of epidermis and dermis and plays a crucial role in protection. Due to factors like UV exposure, there is rising global incidence and mortality of skin cancer and thus there is requirement for advanced treatment methods beyond current methods. So, leading to the exploration of nanotechnology-driven therapies that provide improved diagnostic capabilities, targeted drug delivery and minimized adverse effects. Photodynamic therapy (PDT) uses photosensitizers that is activated by specific light wavelengths and offers minimal invasive treatment with the help of production of reactive oxygen species that destroy cancer cells. The challenges faced by conventional PDT is overcome by advancements such as targeted delivery systems, nanocarriers and oxygen-enhancing strategies. Photothermal therapy (PTT) is also a minimally invasive cancer treatment that uses absorbing agents like nanoparticles which absorb near-infrared (NIR) light. It destroys the cancer cells whiles sparing the healthy tissue by generation of localized heat. Nanotechnology significantly enhances both photodynamic therapy (PDT) and photothermal therapy (PTT) by controlled drug release, precise tumour targeting and improved heat agent or photosensitizer stability. With the use of advanced nanocarriers and NIR-responsive systems greater efficacy in hypoxic environments, reduced side effects and deeper tissue penetration is achieved when compared to conventional approaches. While clinical translation faces challenges like scalability, standardization and safety ongoing advancements in theranostics, AI and biodegradable nanomaterials has the potential to enhance therapeutic outcomes by overcoming these drawbacks.

One of the most common cancers in the world is still skin cancer, which includes both melanoma and non-melanoma forms like squamous cell carcinoma and basal cell carcinoma. Non-specificity, drug resistance, recurrence, and patient response variability are some of the drawbacks of traditional treatment modalities. In order to overcome these obstacles and improve the treatment of skin cancer, this review investigates the combination of personalized nanomedicine and patient-centric care models. Patient-centric approaches place a high value on shared decision-making, customized treatment plans, and ongoing feedback via mobile health technologies and patient-reported outcome measures (PROMs). At the same time, personalized nanomedicine uses sophisticated nanocarriers like liposomes, dendrimers, and gold nanoparticles to deliver targeted, effective, and less toxic therapies by utilizing molecular profiling and biomarker-guided strategies. When these paradigms are applied in concert, precise drug delivery is made possible, therapeutic results are improved, and treatments are tailored to the biological and psychosocial characteristics of the patients. The potential for these integrative approaches to revolutionize standard care in dermatologic oncology is highlighted in this paper along with their recent developments, clinical uses, and potential future directions.

The integration of machine learning (ML) approaches with immune biomarker research may facilitate the identification of candidate markers for achieving personalized medicine approaches in severe mental illnesses (SMI). This systematic review synthesizes the available evidence on ML algorithms applied to immune biomarkers in major depressive (MDD), bipolar (BD) and schizophrenic spectrum disorders (SZ), examining their performance across different clinical uses including diagnostic, prediction, monitoring, prognostic categories, in accordance with the Food and Drug Administration - Biomarker, EndpointS, and other Tools (FDA BEST) framework. We performed a PRISMA-compliant systematic search of PubMed, Web of Science, Scopus and PsycINFO databases until 14 July 2025, including 43 eligible studies with a total sample of 11,556 participants, 8339 with SMI (3228 MDD, 2614 BD and 2497 SZs) and 3217 healthy controls. We systematically described population, ML input data (including blood collection conditions, pre-processing steps, sample type, laboratory assay, missing data, and multimodality), and algorithms (supervised versus unsupervised models, feature selection, validation strategy, outcomes, and performance metrics). Overall, ML models showed moderate to high but heterogeneous performance. Diagnostic applications were the most common (AUC = 0.650-0.990), though predictive, monitoring, and prognostic uses were underrepresented and more variable. Across disorders, pro-inflammatory markers (IL-6, IL-8, TNF-α, IFN-γ, CRP) and IL-10 emerged most consistently, and data-driven approaches suggested shared immune subtypes beyond categorical diagnoses. However, substantial methodological and biological heterogeneity was observed, including inconsistent handling of missing data, limited external validation, and variable feature selection. Immunology-specific sources of variability (such as fasting status, circadian rhythms, and measurement batch effects) were rarely addressed, and the long-term stability of immune-based ML signatures remains largely unexplored. These gaps currently limit clinical translation and underscore the need for standardized protocols and more rigorous ML pipelines.

The human visual system can estimate the three-dimensional shapes of translucent objects. However, the shape estimation of translucent objects is less accurate than that of opaque objects. The specular component is particularly important in the shape perception of translucent objects because it is robust, whereas the non-specular component is affected by translucency. Previously, we developed a shape recovery algorithm as a computational model of human shape perception from opaque specular images. The algorithm primarily uses the specular component and secondarily uses the non-specular component. In the current study, the shape recovery performance of this algorithm for translucent objects was evaluated against ground-truth shapes. The results showed that the reconstruction of the three-dimensional shapes of low-translucency objects by the algorithm was comparable to that of opaque specular images. A modification of the algorithm involving the reversal of the non-specular component was effective for high-translucency objects. However, even with this modification, the estimation accuracy for high-translucency objects was lower than that for opaque objects. These results provide new insight into the possible mechanisms of shape perception for translucent objects.

We present a novel acceleration scheme capable of accelerating electrons and ions in an underdense plasma. Transversely Pumped Acceleration (TPA) uses multiple arrays of counter-propagating laser beamlets that focus onto a central acceleration axis. Tuning the injection timing and the spacing between the adjacent beamlets allows for precise control over the position and velocity of the intersection point of the counter-propagating beam arrays. This results in an accelerating structure that propagates orthogonal to the direction of laser propagation. We present the theory that sets the injection timing of the incoming pulses to accelerate electrons and ions with a tunable phase velocity plasma wave. Simulation results are also presented which demonstrate 1.12 GeV proton beams accelerated in 3.6 mm of plasma and electron acceleration gradients on the order of 1 TeV/m in a scheme that circumvents dephasing. This work has potential applications as a compact accelerator for medical physics and high energy physics colliders.

Rare earth elements (REEs) are increasingly released into the environment due to intensive mining, industrial processing, and expanding technological applications, resulting in widespread human exposure. Within the respiratory exposome framework, REEs have increasingly been recognized as a potentially important class of airborne contaminants. Fine and ultrafine REE-containing particles can penetrate deeply into the distal lung, where they exhibit high biopersistence and limited clearance. Epidemiological evidence from mining and industrial regions suggests that elevated internal REE burdens may be associated with increased prevalence of respiratory symptoms and chronic lung diseases, including bronchitis and interstitial lung disease. Toxicokinetic and experimental studies provide mechanistic support, demonstrating that inhaled REEs preferentially deposit in the alveolar region, interact with epithelial and immune cells, and may translocate into systemic circulation. At the molecular level, REEs have been shown to induce oxidative stress, immune and inflammatory dysregulation, and calcium homeostasis imbalance in experimental models, thereby promoting tissue injury and remodeling. These processes may contribute to a progressive pathological continuum from persistent inflammation to fibrosis and, potentially, tumorigenesis. Notably, exposure characteristics-including particle physicochemical properties, dose, co-exposure scenarios, and host susceptibility-critically shape health outcomes in real-world settings. Despite accumulating evidence, key uncertainties remain regarding human-relevant exposure thresholds, long-term dose-response relationships, and validated biomarkers of effect. Current knowledge is still largely derived from experimental models, with limited integration into population-based risk assessment. Overall, this review uses a structured literature search and narrative synthesis approach to integrate environmental exposure pathways, toxicokinetic characteristics, and mechanistic evidence within an exposome-oriented framework. It highlights that REEs represent emerging inhalation hazards with the potential to contribute to the burden of chronic respiratory diseases, underscoring the need for improved exposure assessment, biomonitoring strategies, and evidence-based public health interventions.

The Internet of Things (IoT) allows continuous health monitoring through the interconnection of wearable and medical devices with computing and storage infrastructures. As cyber threats grow and the sensitivity of healthcare information increases, data integrity, privacy, and access control become critical concerns in digital healthcare environments. This paper proposes a secure IoT-based healthcare framework that integrates machine learning and blockchain techniques for data protection and intelligent health risk assessment.The proposed framework uses cryptographic hashing, Merkle tree construction, digital signatures, and a threshold-based blockchain validation mechanism to enhance the integrity and secure handling of healthcare data in a permissioned simulation environment. The blockchain validation mechanism is evaluated through simulated tampering, replay, and high-load scenarios to assess the integrity verification capability of the proposed framework.Various machine learning models are trained and evaluated on medical datasets to predict disease risk, and their performance is measured using accuracy, precision, recall, and F1-score metrics. The framework is implemented in Python and deployed in a scalable cloud-based environment. Experimental results demonstrate that the proposed framework improves healthcare data integrity verification and supports reliable predictive performance for secure digital healthcare applications.

Antimicrobial resistance (AMR) is a serious public health concern that is threatening the efficacy of existing treatments and raising mortality, morbidity, and financial costs globally. Even though they are essential, traditional antibiotics have serious disadvantages like toxicity, poor solubility, limited stability, and the quick emergence of resistant bacterial strains. As a result, ionic liquids (ILs), the salts having melting points lower than 100 °C and highly versatile physicochemical characteristics, have been thoroughly studied as substitute antibacterial agents. Although ILs have shown broad-spectrum antimicrobial activity and the ability to circumvent resistance mechanisms, their biomedical application is hampered by the variable cytotoxicity of many of the frequently studied ILs. Non-toxic, biocompatible ILs made with natural or generally recognized as safe (GRAS) ions, like choline, amino acids, carbohydrates, and vitamins, have become competitive substitutes for conventional ILs in order to address this issue. The antibacterial mechanisms of non-toxic ionic liquids, such as membrane disruption, enzyme inhibition, and nucleic acid interference, are described in this review along with their biomedical uses, including drug solubilization, implant coatings, wound dressings, and sophisticated drug delivery systems. It highlights methods for lowering the toxicity of conventional antibacterial ILs by substituting toxic ions with safe, biocompatible substitutes emphasizing how non-toxic ILs could serve as an effective means to deal with AMR.

Simultaneous cannabis and alcohol use (co-use) is a public safety concern. Controlled data on the effects of co-ingestion of oral cannabis products (edibles) with alcohol are lacking, despite an increased prevalence of this behavior. To evaluate the individual and interactive effects of cannabis edibles and alcohol on simulated driving and subjective and objective impairment measures. This within-participant, double-blind, double-dummy crossover study of healthy adults included 7 outpatient sessions, separated by 1 week, at Johns Hopkins University School of Medicine from February 2022 to August 2025. Brownies containing 0 mg, 10 mg, or 25 mg Δ9-tetrahydrocannabinol (THC) combined with placebo drinks or alcohol-containing drinks, calculated to achieve breath alcohol concentrations (BrACs) of 0%, 0.05%, or 0.08%. Driving outcomes included the global drive score (GDS), a composite index of multiple driving measures, and the standard deviation of lateral position as the main outcomes. Other outcomes included cumulative impairment clues on standardized field sobriety tests (SFSTs), subjective drug effects, cognitive and psychomotor performance (using the DRUID [Driving Under the Influence of Drugs] application), and blood cannabinoid concentrations. Participants included 25 healthy adults (15 males [60%]; mean [SD] age, 25.6 [4.9] years) who reported recent binge drinking, prior cannabis and alcohol co-use, and fewer than 3 cannabis uses per week. Compared with placebo, all active drug conditions except 10 mg THC negatively impacted driving performance (ie, GDS). Driving impairment from alcohol alone at 0.08% BrAC was comparable with that of 0.05% BrAC and 10 mg THC (mean [SD] GDS, 1.6 [1.6] vs 1.6 [1.4]) and significantly lower than 0.05% BrAC and 25 mg THC (mean [SD] GDS, 2.5 [1.7]; P = .02). Driving impairment and subjective intoxication (eg, confidence to drive) were often greater under co-use conditions compared with cannabis or alcohol alone. Relative to placebo, SFST performance worsened at 0.08% BrAC (mean [SD] score, 2.2 [2.2] vs 0.2 [1.3]; P = .008) but not in several other conditions in which marked driving decrements were observed. THC and metabolite pharmacokinetics were not influenced by alcohol. In this crossover trial of healthy adults who co-used cannabis and alcohol, cannabis edibles combined with alcohol augmented driving impairment. The legal alcohol intoxication limit in most of the US (0.08% BrAC) may be too liberal if a driver has co-used cannabis and alcohol. In this era of expanding cannabis legalization, there is a pressing public health need for improved impairment detection strategies and consideration of cannabis and alcohol co-use in policies dictating access to these substances. ClinicalTrials.gov Identifier: NCT04931095.

Interindividual variations in drug metabolism involve various factors, including posttranscriptional gene regulation mechanisms controlled by microRNAs (miRNAs or miRs) derived from the genome. The aim of this study was to use RNA bioengineering technology to produce novel recombinant human miR-491-5p, miR-491-3p, and pre-miR-491 molecules, namely BioRNA/miR-491-5p, BioRNA/miR-491-3p, and BioRNA/pre-miR-491, respectively, and define their functional difference in regulating UDP-glucuronosyltransferase 1A1 (UGT1A1) expression and drug-metabolizing capacity. All 6 BioRNAs were heterologously overexpressed in Escherichia coli (>30% of total RNA) and isolated by fast protein liquid chromatography to high purity (>97%). As BioRNA/pre-miR-491 agents were processed to both 5p and 3p strands in Hep3B and HepG2 cells, BioRNA/miR-491-5p and -3p were selectively processed to 5p and 3p, respectively, and each accumulated to greater levels. Immunoblotting and immunofluorescence studies demonstrated the efficacy of BioRNA/miR-491-3p to suppress UGT1A1 protein levels in Hep3B and HepG2 cells, localized on the endoplasmic reticulum, exhibiting monomeric (∼55 kDa) and oligomeric (∼150 kDa) bands under different conditions, whereas BioRNA/pre-miR-491 and miR-491-5p had no effects. Using a fluorescent substrate, N-butyl-4-(4-hydroxyphenyl)-1,8-naphthalimide, lower UGT1A1 drug-metabolizing capacities were found in cells treated with BioRNA/miR-491-3p. In addition, liquid chromatography-tandem mass spectrometry analysis revealed a 45% reduction of estradiol 3-glucuronidation activity by BioRNA/miR-491-3p in Hep3B cells, whereas formation of estradiol 17-glucuronidation mediated by other UGTs was unchanged. Together, these results underline the role of miR-491-3p in regulating UGT1A1 and its impact on cellular drug-metabolizing capacity while demonstrating the applications of recombinant miRNA agents to delineating the importance of posttranscriptional gene regulation in drug metabolism. SIGNIFICANT STATEMENT: Research on posttranscriptional gene regulation mainly uses miRNA mimics chemically synthesized in vitro. This study successfully produced 6 novel recombinant miR-491 molecules through in vivo fermentation with transfer RNA scaffold and transfer RNA-fused pre-miRNA carrier-based technologies, which were further utilized to delineate the biogenesis and function of miR-491-3p versus -5p in modulating UDP-glucuronosyltransferase 1A1 protein levels and drug-metabolizing capacity. The findings demonstrate the role of miR-491-3p in regulating UDP-glucuronosyltransferase 1A1 and value of recombinant miRNA agents for studying drug metabolism.

Distal femoral resection is a determinant of coronal alignment and extension-gap symmetry in total knee arthroplasty (TKA). Conventional intramedullary (IM) alignment relies on a fixed femoral canal-referenced valgus angle, which can be affected by anatomical variability and does not account for extension-gap ligament tension. By contrast, a ligament-tension-guided extramedullary (EM) workflow preserves the femoral canal and uses full-extension tension feedback rather than a preset angle to guide coronal positioning. This single-centre retrospective cohort study analysed 76 unilateral primary TKAs performed by a single senior surgeon between September 2019 and January 2024 (EM, n&#x2009;=&#x2009;37; IM, n&#x2009;=&#x2009;39). In the EM group, coronal guide positioning was based on full-extension tension feedback without a preset valgus angle, whereas the IM group used conventional intramedullary alignment with a fixed 6&#xb0; femoral canal-referenced valgus setting. The primary outcome was coronal precision, defined as the absolute deviation of the mechanical lateral distal femoral angle (mLDFA) from 90&#xb0; on standardised full-length weight-bearing radiographs 6 weeks postoperatively. Prespecified secondary outcomes were mLDFA within &#xb1;&#x2009;3&#xb0; of 90&#xb0;, intraoperative visible blood loss, distal femoral resection-step time, and haemoglobin (Hb) decrease within 24&#xa0;h. Compared with conventional IM alignment, the EM workflow was associated with greater coronal precision, with a smaller absolute deviation of the mLDFA from the 90&#xb0; target (P&#x2009;=&#x2009;0.016) and a higher proportion of knees within &#xb1;&#x2009;3&#xb0; of target (P&#x2009;=&#x2009;0.020). Intraoperative visible blood loss and haemoglobin decrease within 24&#xa0;h were lower in the EM group (both P&#x2009;<&#x2009;0.001), whilst distal femoral resection-step time was comparable (P&#x2009;=&#x2009;0.235). Exploratory early recovery measures were favourable in the EM group and should be interpreted cautiously. At 12 months, KSS and WOMAC scores, complication rates, and revision-free status were comparable, and no revision was required in either group. In primary TKA, ligament-tension-guided EM distal femoral resection was associated with greater coronal precision and a lower perioperative bleeding burden than fixed-angle IM alignment, without clear between-group differences in 12-month clinical outcomes. These findings support prospective evaluation of tension-informed coronal target selection.

Ethylenediamine (EDA), a primary biomarker of meat spoilage, has gained considerable attention for its potential uses in the monitoring of meat freshness. Standard analytical approaches have been widely applied for the detection of EDA, meaning that more rapid and convenient detection methods are highly desirable. Here, we introduce an innovative paper-based electrochemical sensor for label-free detection of EDA. To implement a three-electrode system on the surface of paper, silver conductive nano-ink (Ag nano-ink) was drawn by direct pen-on-paper technology. The structure, morphology, and elemental composition of the Ag nano-ink were analyzed using field-emission scanning electron microscopy and energy-dispersive spectroscopy. The presence of the target in the sensing zone changed the electrochemical signal, which was measured using square wave voltammetry. The results demonstrated that the developed sensor detected the target in a linear range from 10 to 1000 &#xb5;M with a low limit of quantification of 10 &#xb5;M. Interestingly, the prepared sensing approach demonstrated a strong ability to determine EDA levels in spoiled beef samples. The selectivity of the designed platform was evaluated by assessing the interference of various amino acids. In addition, the prepared paper-based sensor demonstrated excellent stability for four days. We hope that using Ag nano-ink in a novel electrochemical paper-based sensor for recognizing EDA can open a new window for detecting other biogenic amines in the future.

This study examined how privacy concerns on short-form video platforms influence creativity among communication students through the mediating roles of TikTok use motives and general information technology identity. Guided by privacy calculus theory, uses and gratifications theory, and identity theory, a three-wave longitudinal design was used with 1217 students from three institutions in Chongqing, China. Privacy concerns were measured at Time 1, TikTok use motives and creativity at Time 2, and general information technology identity and creativity at Time 3. Structural equation modeling with full information maximum likelihood estimation tested mediation and sequential mediation models while controlling for demographic variables and baseline creativity. Results showed that privacy concerns negatively predicted creativity, and both TikTok use motives and general IT identity mediated this association. Sequential mediation analysis indicated that TikTok use motives promoted general IT identity, which in turn enhanced creativity. Findings highlight that motivational and identity-based processes jointly explain how privacy concerns shape creative outcomes. The study enriches theory on digital risk and creativity and offers guidance for educators and policymakers seeking to support innovation while protecting digital well-being.

Carbon dot (CD)-based nanozymes have become promising substitutes to natural enzymes in high-performance analytical detection systems, due to their high-water solubility, tunable surface chemistry, and their ability to produce multiple signaling outputs. It has been shown that CDs have a variety of enzyme-mimetic activities, such as peroxidase (POD), oxidase (OXD), and superoxide dismutase (SOD)-like activities, and can be used in bioassays and environmental monitoring. Although such progress has been made, there is still no detailed theoretical framework that explains the origins of their catalytic activity and the mechanisms that underlie sensing selectivity, thus restricting the rational design and optimization of CD-based nanozymes. This is a systematic review of the impact of precursor selection, reaction conditions, and doping strategies on the surface functional groups, defect structures, and metal active centers of CDs. It also explains how these structural features work synergistically to regulate electron transfer processes and active site formation. Furthermore, the review suggests that interfacial noncovalent interactions are the main determinants of the sensing selectivity of CD nanozymes, which is accompanied by molecular recognition processes and energy-level complementation. Recent advances in multimodal signaling strategies to detect complex systems are also mentioned. Lastly, the present issues and future outlooks in the controlled construction and sensing uses of CDs are also pointed out, which can be useful in the rational design and practical use of these new nanozymes.

The right of patients to decline information about their health, prognosis, and available treatment options is a salient principle in both domestic law and international declarations and conventions. This right may be considered either unnegotiable or subject to certain terms and conditions. While respecting this right may seem straightforward, doing so in clinical practice can be challenging. This article uses a realistic hypothetical scenario to examine the epistemic, moral, and practical challenges that can arise, particularly with patients nearing the end of life. These challenges include how and when to honor the right and its potential conflict with moral values such as self-determination, authenticity, and avoiding harm to oneself and others. The end of life exacerbates these issues because of the irretrievability of decisions and the successive reduction of possible courses of action, as well as potentially changing preferences. These potential conflicts of values deserve further attention and must be considered when deciding whether to honor a patient's wish not to know.

Curcumin, a polyphenolic compound from Curcuma longa, has many biological effects, including antioxidant, anti-inflammatory, anticancer, and neuroprotective properties. However, its use in food, pharmaceutical, and biomedical systems is limited owing to poor water solubility, chemical instability, fast metabolism, and very low oral bioavailability. To address these issues, various formulation strategies have been created. Microencapsulation is one of the most effective methods for improving the stability, bioaccessibility, and controlled release of curcumin. At the same time, computational tools such as molecular docking and molecular dynamics simulations have become more important for understanding curcumin-carrier interactions and predicting formulation stability at the molecular level. Although both experimental encapsulation techniques and in silico modeling are well-established, research in these areas often occurs separately, leading to fragmented understanding of curcumin delivery systems. This review offers a detailed analysis of curcumin research by connecting its physicochemical properties and degradation pathways with microencapsulation strategies and computational modeling. Key encapsulation techniques such as spray drying, ionotropic gelation, complex coacervation, and nanostructured delivery systems are examined in terms of their mechanisms, benefits, drawbacks, and uses. Additionally, recent progress in molecular docking and molecular dynamics simulations is discussed to emphasize their growing role in helping choose carriers and design formulations. By linking formulation science with predictions at the molecular level, this review presents a framework to promote the development of effective, stable, and bioavailable curcumin-based delivery systems for food, pharmaceutical, and biomedical purposes.

MALDI-TOF MS (Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry) technology has become an effective tool in clinical mycology laboratories in recent years for the identification of pathogenic filamentous fungi. A total of 527 clinical specimens, comprising samples from both superficial and deep mycoses, were included in this study. Filamentous fungal samples isolated from various clinical specimens and identified using conventional methods between 2017 and 2018 at the Medical Microbiology Mycology Laboratory of Istanbul University-Cerrahpa&#x15f;a Faculty of Medicine Hospital were analyzed using MALDI Biotyper Bruker Daltonik GmbH Revision 4 and the Filamentous Fungi database v3.0. In the identification of mold samples, the highest scoring identification values &#x200b;&#x200b;were determined with the liquid cultivation and extraction method recommended by MALDI-TOF MS for filamentous fungi. A total of 46 different filament fungi species, including 6 dermatophytes and 40 non-dermatophyte filament fungi species, were identified from the clinical samples in the study. Of the total of 527 clinical samples, 27% (n&#x2009;=&#x2009;142) were identified as having high reliability in the range of 1.8 to 3, and 20% (n&#x2009;=&#x2009;103) as having low reliability in the range of 1.6 to 1.79. The total rate of identified filament fungi was successfully determined as 47% (n&#x2009;=&#x2009;245). In the practical experience of our medical mycology laboratory, we evaluated the current uses of MALDI-TOF MS and their practical application in daily routine. In the identification of mold species obtained from clinical samples by MALDI-TOF, it is important to meticulously prepare sample preparation protocols, use an identification strategy suitable for routine diagnosis, and continuously update libraries using advanced databases.

Heat energy is released during the reversible chemical reaction of the thermochemical heat energy storage (TCHS) system. Thermochemical heat energy uses an anhydrous salt and water vapor, releasing heat as water vapor molecules are absorbed by the anhydrous salt particles, forming a hydrated salt. The amount of heat energy released is proportional to the amount of sorbate that could be absorbed. Therefore, materials suitable for thermochemical heat storage systems are porous and highly thermally conductive to rapidly transfer the heat generated. Today, researchers and scientists are eagerly working to develop novel materials for the system to overcome the limitations of commonly used materials that hinder achieving higher performance. Thus, this review provides a comprehensive review of the methods and materials for high-performance thermochemical heat storage systems. It includes methods ranging from simple mechanical and physical mixing and blending to melt mixing, impregnation, foaming, electrospinning of composite polymers and nanofiber materials, and stabilized cyclic degradation, along with the corresponding results. The fillet materials described are also carbon-based, metallic, and MXene composites, which enhance thermal conductivity, while composite nanofibers, metal-organic frameworks (MOFs), alumina, silica, and zeolite enhance sorption capacity, owing to their high porous surface area-to-volume ratios. It is concluded that materials with optimized porous structures, cyclic stability, and improved heat transfer can be effective for the TCHS system when suitable methods are employed and chemically compatible materials are used.