Search Results (1,266)

Disease resistance is one of the most important target traits for sugarcane genetic improvement. Sugarcane brown stripe (SBS) caused by Helminthosporium stenospilum is one of the most destructive foliar diseases, which not only reduces harvest cane yield but also sugar content. This study aimed to identify quantitative trait loci (QTL) and candidate genes associated with SBS resistance. Here, the phenotypic investigation in six field habitats showed a continuous normal distribution, revealing that the SBS resistance trait is a quantitative trait. Two high-density linkage maps based on the single-dose markers calling from the Axiom Sugarcane100K SNP chip were constructed for the dominant sugarcane cultivars YT93-159 (SBS-resistant) and ROC22 (SBS-susceptible) with a density of 2.53 cM and 2.54 cM per SNP marker, and mapped on 87 linkage groups (LGs) and 80 LGs covering 3069.45 cM and 1490.34 cM of genetic distance, respectively. A total of 32 QTL associated with SBS resistance were detected by QTL mapping, which explained 3.73–11.64% of the phenotypic variation, and the total phenotypic variance explained (PVE) in YT93-159 and ROC22 was 107.44% and 79.09%, respectively. Among these QTL, four repeatedly detected QTL (qSBS-Y38-1, qSBS-Y38-2, qSBS-R8, and qSBS-R46) were considered stable QTL. Meanwhile, two major QTL, qSBS-Y38 and qSBS-R46, could account for 11.47% and 11.64% of the PVE, respectively. Twenty-five disease resistance candidate genes were screened by searching these four stable QTL regions in their corresponding intervals, of which Soffic.01G0010840-3C (PR3) and Soffic.09G0017520-1P (DND2) were significantly up-regulated in YT93-159 by qRT-PCR, while Soffic.01G0040620-1P (EDR2) was significantly up-regulated in ROC22. These results will provide valuable insights for future studies on sugarcane breeding in combating this disease. Full article

Alopecia, a prevalent dermatological disorder affecting over half of the global population, is strongly associated with psychological distress. Extracts from Periplaneta americana (L. PA), a medicinal insect resource, exhibit pharmacological activities (e.g., antioxidant, anti-inflammatory, microcirculation improvement) that align with core therapeutic targets for alopecia. This study aimed to systematically investigate the efficacy and mechanisms of PA extracts in promoting hair regeneration. A strategy combining network pharmacology prediction and in vivo experiments was adopted. The efficacy of a Periplaneta americana extract was validated by evaluating hair regrowth status and skin pathological staining in C57BL/6J mice. Transcriptomics, metabolomics, RT-qPCR, and 16s rRNA techniques were integrated to dissect the underlying mechanisms of its hair-growth-promoting effects. PA-011 significantly promoted hair regeneration in depilated mice via multiple mechanisms: enhanced skin superoxide dismutase activity and upregulated vascular endothelial growth factor expression; modulated FOXO/PI3K/AKT signaling pathway and restored skin microbiota homeostasis; and accelerated transition of hair follicles from the telogen to anagen phase. PA-011 exerts hair-promoting effects through synergistic modulation of FOXO/PI3K/AKT signaling and the skin microbiome. As a novel therapeutic candidate, it warrants further systematic investigation for clinical translation. Full article

Background: Precision medicine refers to the formulation of personalized drug regimens according to the individual characteristics of patients to achieve optimal efficacy and minimize adverse reactions. Additive manufacturing (AM), also known as three-dimensional (3D) printing, has emerged as an optimal solution for precision drug delivery, enabling customizable and the fabrication of multifunctional structures with precise control over morphology and release behavior in pharmaceutics. However, the influence of 3D printing parameters on the printed tablets, especially regarding in vitro and in vivo performance, remains poorly understood, limiting the optimization of manufacturing processes for controlled-release profiles. Objective: To establish the fabrication process of 3D-printed controlled-release tablets via comprehensively understanding the printing parameters using fused deposition modeling (FDM) combined with hot-melt extrusion (HME) technologies. HPMC-AS/HPC-EF was used as the drug delivery matrix and carbamazepine (CBZ) was used as a model drug to investigate the in vitro drug delivery performance of the printed tablets. Methodology: Thermogravimetric analysis (TGA) was employed to assess the thermal compatibility of CBZ with HPMC-AS/HPC-EF excipients up to 230 °C, surpassing typical processing temperatures (160–200 °C). The formation of stable amorphous solid dispersions (ASDs) was validated using differential scanning calorimetry (DSC), hot-stage polarized light microscopy (PLM), and powder X-ray diffraction (PXRD). A 15-group full factorial design was then used to evaluate the effects of the fan speed (20–100%), platform temperature (40–80 °C), and printing speed (20–100 mm/s) on the tablet properties. Response surface modeling (RSM) with inverse square-root transformation was applied to analyze the dissolution kinetics, specifically t_50% (time for 50% drug release) and Q_4h (drug released at 4 h). Results: TGA confirmed the thermal compatibility of CBZ with HPMC-AS/HPC-EF, enabling stable ASD formation validated by DSC, PLM, and PXRD. The full factorial design revealed that printing speed was the dominant parameter governing dissolution behavior, with high speeds accelerating release and low speeds prolonging release through porosity-modulated diffusion control. RSM quadratic models showed optimal fits for t_50% (R² = 0.9936) and Q_4h (R² = 0.9019), highlighting the predictability of release kinetics via process parameter tuning. This work demonstrates the adaptability of polymer composite AM for tailoring drug release profiles, balancing mechanical integrity, release kinetics, and manufacturing scalability to advance multifunctional 3D-printed drug delivery devices in pharmaceutics. Full article

Background: Charcot–Marie–Tooth (CMT) is a group of inherited diseases impairing the peripheral nervous system. CMT originates from genetic variants that affect proteins fundamental for the myelination of peripheral nerves and survival. Moreover, environmental and humoral factors can impact disease development and evolution. Currently, no therapy is available. Metabolomics is an emerging field of biomedical research that enables the development of novel biomarkers for neurodegenerative diseases by targeting metabolic pathways or metabolites. This study aimed to evaluate the metabolomics profile of CMT disease by comparing patients with healthy individuals. Methods: A total of 22 CMT patients (CMT) were included in this study and were demographically matched with 26 healthy individuals (C). Serum samples were analyzed through Nuclear Magnetic Resonance spectroscopy, and multivariate and univariate statistical analyses were subsequently applied. Results: A supervised model showed a clear separation (R²X = 0.3; R²Y = 0.7; Q² = 0.4; p-value = 0.0004) between the two classes of subjects, and nine metabolites were found to be significantly different (2-hydroxybutyrate, 3-hydroxybutyrate, 3-methyl-2-oxovalerate, choline, citrate, glutamate, isoleucine, lysine, and methyl succinate). The combined ROC curve showed an AUC of 0.94 (CI: 0.9–1). Additional altered metabolic pathways were also identified within the disease context. Conclusion: This study represents a promising starting point, demonstrating the efficacy of metabolomics in evaluating CMT patients and identifying novel potential disease biomarkers. Full article

Background/Objectives: Alterations in DNA damage repair mechanisms can impair the therapeutic effectiveness of cisplatin. MicroRNAs (miRNAs), key regulators of DNA damage repair processes, have been proposed as promising biomarkers for predicting the response to platinum-based chemotherapy (CT) in non-small cell lung cancer (NSCLC). In this study, by using a bioinformatics approach, we identified six miRNAs, which were differentially expressed (DE) between NSCLC patients characterized as responders and non-responders to platinum-based CT. We further validated the differential expression of the selected miRNAs on tumor and matched normal tissues from patients with resected NSCLC. Methods: Two miRNA microarray expression datasets were retrieved from the Gene Expression Omnibus (GEO) repository, comprising a total of 69 NSCLC patients (N = 69) treated with CT and annotated data from their response to treatment. Differential expression analysis was performed using the Linear Models for Microarray Analysis (Limma) package in R to identify DE miRNAs between responders (N = 33) and non-responders (N = 36). Quantitative real-time PCR (qRT-PCR) was used to assess miRNA expression levels in clinical tissue samples (N = 20). Results: Analysis with the Limma package revealed 112 DE miRNAs between responders and non-responders. A random-effects meta-analysis further identified 24 miRNAs that were consistently up- or downregulated in at least two studies. Survival analysis using the Kaplan–Meier plotter (KM plotter) indicated that 22 of these miRNAs showed significant associations with prognosis in NSCLC. Functional and pathway enrichment analysis revealed that several of the identified miRNAs were linked to key pathways implicated in DNA damage repair, including the p53, Hippo, PI3K and TGF-β signaling pathways. We finally distinguished a six-miRNA signature consisting of miR-26a, miR-29c, miR-34a, miR-30e-5p, miR-30e-3p and miR-497, which were downregulated in non-responders and are involved in at least three DNA damage repair pathways. Comparative expression analysis on tumor and matched normal tissues from surgically treated NSCLC patients confirmed their differential expression in clinical samples. Conclusions: In summary, we identified a signature of six miRNAs that are suppressed in NSCLC and may serve as a predictor of cisplatin response in NSCLC. Full article

Background: The global increase in type 2 diabetes mellitus (T2DM) cases necessitates the need for early detection of metabolic changes. This study investigated variations in liver enzymes, renal markers, electrolytes, and lipid profiles among T2DM patients stratified by hemoglobin A1c (HbA1c) categories to support early identification and better management of diabetes-related complications. Methods: A retrospective observational study at King Khalid University Hospital (KKUH), Riyadh, included 621 adult patients diagnosed with T2DM categorized into four HbA1c groups: normal (<5.7%), prediabetes (5.7–6.4%), controlled diabetes (6.5–7.9%), and uncontrolled diabetes (≥8.0%). Biochemical parameters included the liver profile: alkaline phosphatase (ALP) and bilirubin, renal profile: creatinine, blood urea nitrogen (BUN), glucose, sodium, and chloride, and lipid profile: cholesterol, high-density lipoprotein (HDL), low-density lipoprotein (LDL), and triglycerides. Regression models identified predictors of ALP, cholesterol, and LDL. Results: ALP was higher in uncontrolled diabetes (89.0 U/L, Q1–Q3: 106.3–72.0) than in the prediabetes group (75.0 U/L, Q1–Q3: 96.8–62.3). Sodium and chloride were lower in uncontrolled diabetes (Na: 138.3 mmol/L, Q1–Q3: 140.3–136.4; Cl: 101.1 mmol/L, Q1–Q3: 102.9–99.4) compared to the normal group (Na: 139.5 mmol/L, Q1–Q3: 142.4–136.9; Cl: 103.5 mmol/L, Q1–Q3: 106.1–101.7). LDL was lower in uncontrolled diabetes (2.1 mmol/L, Q1–Q3: 2.8–1.7) than in the normal group (2.8 mmol/L, Q1–Q3: 3.7–2.2), while triglycerides were higher in patients with uncontrolled diabetes compared to the normal group (1.45 mmol/L, Q1–Q3: 2.02–1.11 vs. 1.26 mmol/L, Q1–Q3: 1.44–0.94). Regression models showed low explanatory power (R² = 2.1–7.3%), with weight, age, and sex as significant predictors of select biochemical markers. Conclusions: The study observed biochemical variations across HbA1c categories in T2DM patients, likely reflecting insulin resistance. Monitoring these markers in conjunction with HbA1c can enhance early detection and improve the management of complications. Full article

Neisseria gonorrhoeae, the causative agent of gonorrhea, represents a major public health concern due to its increasing antimicrobial resistance. While often asymptomatic—particularly in extragenital infections—untreated cases can lead to severe complications and further transmission. Despite global efforts to monitor antimicrobial resistance, data on the molecular determinants underlying decreased susceptibility in N. gonorrhoeae remain scarce in Peru. This study aimed to detect mutations in the penA and 23S rRNA genes, which confer decreased susceptibility to cephalosporins and azithromycin resistance. We extracted DNA from 124 N. gonorrhoeae-positive clinical rectal specimens collected in Aptima Combo 2 transport tubes from MSM patients. These DNA samples were then screened using the Mismatch Amplification Mutation Assay-based real-time PCR (MAMA-qPCR) to identify mutations in the 23S rRNA and penA genes. Each sample underwent separate reactions to detect A2059G and C2611T mutations in the 23S rRNA gene, and 86 of these samples were further tested in individual qPCR assays for the penA D345 deletion (D345del) or G545S mutations. Sanger sequencing was performed on all DNA samples positive for 23S rRNA mutations by MAMA-qPCR assay, and on 27 DNA samples that yielded sufficient penA amplicons for additional sequencing. Using the MAMA-qPCR assay for the 23S rRNA gene, 64 of 124 samples amplified in the A2059G reaction: 2 (3.1%) carried the mutation, and 62 were classified as wild type. In the C2611T reaction, 42 of 124 samples amplified, and none of them carried the mutation. Using the MAMA-qPCR assay for the penA gene, we only analyzed 86 samples, as the remaining 38 samples had insufficient DNA yield. A total of 44 of the 86 samples amplified in the D345del reaction: 5 (11.4%) carried the D345del, and 39 were classified as wild type. In the G545S reaction, 4 (6.4%) carried the mutation, and 58 were classified as wild type. Finally, sequencing of the penA gene in the 27 samples revealed mutations related to decreased susceptibility to cephalosporins. This study identified genetic mutations conferring resistance to azithromycin and decreased susceptibility to cephalosporins, providing an overview of the circulating mutations conferring resistance in N. gonorrhoeae strains in Peru. Full article

Human-Robot Collaboration (HRC) is pivotal for flexible, worker-centric manufacturing in Industry 5.0, yet dynamic task allocation remains difficult because operator states—fatigue and skill—fluctuate abruptly. I address this gap with a hybrid framework that couples real-time perception and double-estimating reinforcement learning. A Convolutional Neural Network (CNN) classifies nine fatigue–skill combinations from synthetic physiological cues (heart-rate, blink rate, posture, wrist acceleration); its outputs feed a Double Deep Q-Network (DDQN) whose state vector also includes task-queue and robot-status features. The DDQN optimises a multi-objective reward balancing throughput, workload and safety and executes at 10 Hz within a closed-loop pipeline implemented in MATLAB R2025a and RoboDK v5.9. Benchmarking on a 1000-episode HRC dataset (2500 allocations·episode⁻¹) shows the hybrid CNN+DDQN controller raises throughput to 60.48 ± 0.08 tasks·min⁻¹ (+21% vs. rule-based, +12% vs. SARSA, +8% vs. Dueling DQN, +5% vs. PPO), trims operator fatigue by 7% and sustains 99.9% collision-free operation (one-way ANOVA, p < 0.05; post-hoc power 1 − β = 0.87). Visual analyses confirm responsive task reallocation as fatigue rises or skill varies. The approach outperforms strong baselines (PPO, A3C, Dueling DQN) by mitigating Q-value over-estimation through double learning, providing robust policies under stochastic human states and offering a reproducible blueprint for multi-robot, Industry 5.0 factories. Future work will validate the controller on a physical Doosan H2017 cell and incorporate fairness constraints to avoid workload bias across multiple operators. Full article

Background/Objectives: The resistance mutations EGFR^{L858R/T790M/C797S} in epidermal growth factor receptor (EGFR) are key factors in the reduced efficacy of Osimertinib. Predicting the inhibitory effects of Osimertinib derivatives against these mutations is crucial for the development of more effective inhibitors. This study aims to predict the inhibitory effects of Osimertinib derivatives against EGFR^{L858R/T790M/C797S} mutations. Methods: Six models were established using heuristic method (HM), random forest (RF), gene expression programming (GEP), gradient boosting decision tree (GBDT), polynomial kernel function support vector machine (SVM), and mixed kernel function SVM (MIX-SVM). The descriptors for these models were selected by the heuristic method or XGBoost. Comprehensive learning particle swarm optimizer was adopted to optimize hyperparameters. Additionally, the internal and external validation were performed by leave-one-out cross-validation ($Q_{LOO}^2$), 5-fold cross validation ($Q_{5-fold}^2$) and concordance correlation coefficient (CCC), $Q_{F1}^2$, and $Q_{F2}^2$. The properties of novel EGFR inhibitors were explored through molecular docking analysis. Results: The model established by MIX-SVM whose kernel function is a convex combination of three regular kernel functions is best: $R^2$ and RMSE for training set and test set are 0.9445, 0.1659 and 0.9490, 0.1814, respectively; $Q_{LOO}^2$, $Q_{5-fold}^2$, CCC, $Q_{F1}^2$, and $Q_{F2}^2$ are 0.9107, 0.8621, 0.9835, 0.9689, and 0.9680. Based on these results, the IC₅₀ values of 162 newly designed compounds were predicted using the HM model, and the top four candidates with the most favorable physicochemical properties were subsequently validated through PEA. Conclusions: The MIX-SVM method will provide useful guidance for the design and screening of novel EGFR^{L858R/T790M/C797S} inhibitors. Full article

This work systematically altered the molar ratio of CaO and MgO (R = [CaO]/[(CaO + MgO)], mol%) to elucidate the underlying mechanisms driving the observed changes in macroscopic properties. The results indicated that as CaO increasingly replaced MgO, the rise in the content of non-bridging oxygen led to the depolymerization of the glass structure. A quantitative analysis of Qⁿ units in the [SiO₄] tetrahedron using ²⁹Si MAS NMR revealed that a non-monotonic variation appeared when the Q⁴ unit reached a minimum at R = 0.7. Meanwhile, the chemical environment of aluminum also varies with the R, and the presence of high-coordinated aluminum species is observed when Ca²⁺ and Mg²⁺ ions coexist. In terms of overall performance, both density and molar volume exhibited a linear trend. However, thermal stability, viscosity, characteristic temperatures (including melting temperature, Littleton softening temperature, working point temperature, and glass transition temperature), and mechanical properties showed deviations from linearity. Additionally, four non-isothermal thermodynamics was employed to quantitatively assess the thermal stability of samples C-0.7 and C-1. The insights gained from this study will aid in the development of advanced glass materials with tailored properties for industrial applications. Full article

The postmortem interval (PMI), defined as the time elapsed between death and the discovery or examination of the body, is a crucial parameter in forensic science for estimating the time of death. There are many ways to measure the PMI, such as Henssge’s nomogram, which uses rectal temperature measurement; livor mortis; rigor mortis; and forensic entomology. However, these methods are usually affected by various conditions in the surrounding environment. The purpose of the present study was to compare molecular genetics and histological changes in the brain and skeletal muscle tissues of SD rats over increasing periods of time after death. For the PMIs, we considered 0 h, 6 h, 12 h, 24 h, 36 h, 48 h, 4 days, 6 days, 8 days, 10 days, 14 days, and 21 days and compared them at 4 °C and 26 °C. Hematoxylin and Eosin (H&E) staining was performed to observe tissue changes. Morphological tissue changes were observed in cells for up to 21 days at 4 °C, and cell destruction was visually confirmed after 14 days at 26 °C. Total RNA (tRNA) was isolated from each tissue sample, and complementary DNA (cDNA) was synthesized. A reverse transcription quantitative PCR (RT-qPCR) SYBR Green assay targeting three types of housekeeping genes, including Gapdh, Sort1, B2m, and 5S rRNA, was performed. The results showed that Gapdh and 5S rRNA were highly stable and could be better RNA targets for estimating the PMI in brain and skeletal muscle tissues. Conversely, Sort1 and B2m showed poor stability and low expression levels. In conclusion, these molecular biomarkers could be used as auxiliary indicators of the PMI in human, depending on the stability of the marker. Full article

Silicon carbide (SiC) half-bridge power modules are widely utilized in new energy power generation, electric vehicles, and industrial power supplies. To address the research gap in collaborative validation between electro-thermal coupling models and process reliability, this paper proposes a closed-loop methodology of “design-simulation-process-validation”. This approach integrates in-depth electro-thermal simulation (LTspice XVII/COMSOL Multiphysics 6.3) with micro/nano-packaging processes (sintering/bonding). Firstly, a multifunctional double-pulse test board was designed for the dynamic characterization of SiC devices. LTspice simulations revealed the switching characteristics under an 800 V operating condition. Subsequently, a thermal simulation model was constructed in COMSOL to quantify the module junction temperature gradient (25 °C → 80 °C). Key process parameters affecting reliability were then quantified, including conductive adhesive sintering (S820-F680, 39.3 W/m·K), high-temperature baking at 175 °C, and aluminum wire bonding (15 mil wire diameter and 500 mW ultrasonic power/500 g bonding force). Finally, a double-pulse dynamic test platform was established to capture switching transient characteristics. Experimental results demonstrated the following: (1) The packaged module successfully passed the 800 V high-voltage validation. Measured drain current (4.62 A) exhibited an error of <0.65% compared to the simulated value (4.65 A). (2) The simulated junction temperature (80 °C) was significantly below the safety threshold (175 °C). (3) Microscopic examination using a Leica IVesta 3 microscope (55× magnification) confirmed the absence of voids at the sintering and bonding interfaces. (4) Frequency-dependent dynamic characterization revealed a 6 nH parasitic inductance via Ansys Q3D 2025 R1 simulation, with experimental validation at 8.3 nH through double-pulse testing. Thermal evaluations up to 200 kHz indicated 109 °C peak temperature (below 175 °C datasheet limit) and low switching losses. This work provides a critical process benchmark for the micro/nano-manufacturing of high-density SiC modules. Full article

Nero Siciliano (NS) is an autochthonous pig breed reared in northeastern Sicily; despite its high-quality meat products, NS is currently endangered. This study aimed to evaluate the genetic variability at nine loci within candidate genes for meat traits—Melanocortin 4 Receptor (MC4R), Ryanodine Receptor 1 (RYR1), Class 3 Phosphoinositide 3-Kinase (PIK3C3) and Leptin (LEP)—to provide useful information for preservation and exploitation of the NS pig breed. Distribution of the genetic variants was assessed in a representative sample of 87 pigs (18 boars and 69 sows) collected in nine farms located in the original breeding area. Genotypes have been determined using PCR-RFLP and Sanger sequencing. Alleles linked to different growth rates and back fat deposition showed high frequencies (MC4R c.175C—0.93; LEP g.3469T—0.91) in the whole sample. Deviations from Hardy–Weinberg equilibrium and different allele distribution in boars and sows were observed. The RYR1 g.1843T allele, associated with Malignant Hyperthermia and Pale Soft Exudative meat defect, was reported in seven heterozygote pigs (q = 0.04) with one farm exhibiting a frequency of 0.29. Our results suggest the need for continuous monitoring of the genetic variants in NS both to maintain high meat quality and eradicate the RYR1 g.1843T allele. Full article

Pelvic organ prolapse (POP) is a common pelvic floor disorder with substantial impact on women’s quality of life, necessitating accurate and reproducible diagnostic methods. This study investigates the use of three-dimensional (3D) transperineal ultrasound, integrated with artificial intelligence (AI), to evaluate pelvic floor biomechanics and identify correlations between biometric parameters and prolapse severity. Thirty-seven female patients diagnosed with genital prolapse (mean age: 65.3 ± 10.6 years; mean BMI: 29.5 ± 3.8) were enrolled. All participants underwent standardized 3D transperineal ultrasound using the Mindray Smart Pelvic system, an AI-assisted imaging platform. Key biometric parameters—anteroposterior diameter, laterolateral diameter, and genital hiatus area—were measured under three functional states: rest, maximal Valsalva maneuver, and voluntary pelvic floor contraction. Additionally, two functional indices were derived: the distensibility index (ratio of Valsalva to rest) and the contractility index (ratio of contraction to rest), reflecting pelvic floor elasticity and muscular function, respectively. Statistical analysis included descriptive statistics and univariate correlation analysis using Pelvic Organ Prolapse Quantification (POP-Q) system scores. Results revealed a significant correlation between laterolateral diameter and prolapse severity across multiple compartments and functional states. In apical prolapse, the laterolateral diameter measured at rest and during both Valsalva and contraction showed positive correlations with POP-Q point C, indicating increasing transverse pelvic dimensions with more advanced prolapse (e.g., r = 0.42 to 0.58; p < 0.05). In anterior compartment prolapse, the same parameter measured during Valsalva and contraction correlated significantly with POP-Q point AA (e.g., r = 0.45 to 0.61; p < 0.05). Anteroposterior diameters and genital hiatus area were also analyzed but showed weaker or inconsistent correlations. AI integration facilitated real-time image segmentation and automated measurement, reducing operator dependency and increasing reproducibility. These findings highlight the laterolateral diameter as a strong, reproducible anatomical marker for POP severity, particularly when assessed dynamically. The combined use of AI-enhanced imaging and functional indices provides a novel, standardized, and objective approach for assessing pelvic floor dysfunction. This methodology supports more accurate diagnosis, individualized management planning, and long-term monitoring of pelvic floor disorders. Full article

Herpes simplex virus 2 (HSV-2) remains a significant cause of morbidity and mortality in immunocompromised individuals, despite the availability of effective antivirals. Infections caused by drug-resistant isolates are an emerging concern among these patients. Understanding evolutionary aspects of HSV-2 resistance is crucial for designing improved therapeutic strategies. Here, we characterized 11 HSV-2 isolates recovered from various body sites of a single immunocompromised patient suffering from a primary HSV-2 infection unresponsive to acyclovir and foscarnet. The isolates were analyzed phenotypically and genotypically (Sanger sequencing of viral thymidine kinase and DNA polymerase genes). Viral clone isolations, deep sequencing, viral growth kinetics, and dual infection competition assays were performed retrospectively to assess viral heterogeneity and fitness. Sanger sequencing identified mixed populations of DNA polymerase mutant variants. Viral clones were plaque-purified and genotyped, revealing 17 DNA polymerase mutations (K533E, A606V, C625R, R628C, A724V, S725G, S729N, I731F, Q732R, M789T/K, Y823C, V842M, R847C, F923L, T934A, and R964H) associated with acyclovir and foscarnet resistance. Deep-sequencing of the DNA polymerase detected drug-resistant variants ranging between 1 and 95%, although the first two isolates had a wild-type DNA polymerase. Some mutants showed reduced fitness, evidenced by (i) the frequency of variants identified by deep-sequencing not correlating with the proportion of mutants found by plaque-purification, (ii) loss of the variants upon passaging in cell culture, or (iii) reduced frequencies in competition assays. This study reveals the rapid evolution of heterogeneous drug-resistant HSV-2 populations under antiviral therapy, highlighting the need for alternative treatment options and resistance surveillance, especially in severe infections. Full article