Nevin Manimala

AAPS PharmSciTech. 2026 Jan 15;27(1):74. doi: 10.1208/s12249-025-03300-7.

ABSTRACT

Vildagliptin (VLD) is a powerful oral hypoglycemic agent used in the management of type II diabetes. The goal of the current research was to develop VLD-loaded zein protein-based nanoparticles (VLD-ZP NPs) for enhancing their oral hypoglycemic effect, achieving a sustained release profile, and addressing the issues associated with rapid metabolism and side effects. A 2³ full factorial design was utilized to assess the influence of independent formulation variables on the observed responses. The independent variables considered were VLD-to-zein weight ratio (X₁), ethanol-to-water volume ratio (X₂), and stirring time (X₃). The dependent responses evaluated were particle size (Ps), zeta potential (Zp), entrapment efficiency (EE), and percent of drug release after 2H (Q_2H) and 8H (Q_8H). The optimized VLD-ZP NPs formula (F04), with a desirability value of 0.94, exhibited a small Ps (149.64 ± 1.4nm), low Q_2H (23.97 ± 2.1%), high Zp (- 37.67 ± 1.8mV), high EE (68.67 ± 2.3%), and sustained Q_8H release (62.57 ± 2.4%). Further investigations of F04 confirmed sustained drug release, spherical vesicle morphology through TEM, and effective entrapment via DSC and X-ray diffraction. In vivo pharmacokinetic studies revealed that C_max and AUC_0-12H of F04 were enhanced by 1.25-fold and 1.8-fold compared to the marketed VLD product. Also, t_1/2 and MRT were extended by 1.84-fold and 1.56-fold, respectively. These findings indicated improved oral bioavailability and prolonged residence time of VLD. Additionally, the in vivo pharmacodynamic study revealed that F04 provided markedly superior and sustained hypoglycemic effects over the marketed VLD product, with higher R_max, longer TR_½, and a 2.8-fold increase in AUC_(0-24H).

PMID:41540169 | DOI:10.1208/s12249-025-03300-7

Chin J Integr Med. 2026 Jan 15. doi: 10.1007/s11655-026-4137-5. Online ahead of print.

ABSTRACT

OBJECTIVE: To assess the effectiveness and safety of Biqi Capsules in the treatment of rheumatoid arthritis (RA).

METHODS: This multicenter, prospective cohort study was conducted across more than 100 centers in China. Data on RA patients were collected from the CERTAIN database between January 2019 and March 2024. Patients were categorized into an exposed group and a control group. The control group was treated with conventional Western medicine, and the exposed group was combined with Biqi Capsules (0.3 g/capsule, 4 capsules per dose, orally, 2-3 times/d) on the basis of conventional treatment. Propensity score matching (PSM) was applied to balance baseline characteristics. The main outcomes was the Disease Activity Score-28 (DAS28-ESR), and secondary outcomes included Health Assessment Questionnaire (HAQ), Visual Analogue Scale (VAS) for pain, tender joint count (TJC), swollen joint count (SJC), Patient Global Assessment (PGA), and Evaluator Global Assessment (EGA) scores, as well as erythrocyte sedimentation rate (ESR), and other laboratory test results as safety indicators.

RESULTS: A total of 2,267 patient records were analyzed, with 711 in the exposed group and 1,556 in the control group. After PSM, 710 patients were included in each group, with comparable baseline demographic characteristics (P>0.05). Following matching, pre-treatment HAQ and TJC were similar between groups (P>0.05), while significant differences were observed in DAS28-ESR, EGA, PGA, VAS, SJC, TJC, and ESR (P<0.01). Post-treatment analysis showed that all indices improved significantly in both groups (P<0.01). Furthermore, post-treatment levels of EGA, PGA, VAS, SJC, and TJC were statistically significant between the two groups (P<0.01). The reduction in DAS28-ESR was significantly greater in the exposed group than in the control group (P<0.01). Statistically greater improvements were also found in EGA, PGA, SJC, TJC, and ESR, indicating superior clinical improvement in the exposed group (P<0.01). The incidence of abnormal γ-glutamyl transferase and creatinine levels were higher in the control group than in the exposed group (P<0.01 or P<0.05), while no significant differences were observed in other safety indicators between the two groups (P>0.05).

CONCLUSION: Biqi Capsules combined with conventional treatment of Western medicine effectively reduce RA disease activity, lower inflammation levels, relieve clinical symptoms, and do not increase the incidence of adverse events. (Registration No. NCT05219214).

PMID:41540164 | DOI:10.1007/s11655-026-4137-5

Pediatr Nephrol. 2026 Jan 15. doi: 10.1007/s00467-025-07138-w. Online ahead of print.

ABSTRACT

BACKGROUND: Limited data exist on the use of novel iron therapies in children with chronic kidney disease (CKD). We conducted a cross-over study to compare iron polymaltose complex (IPC) and liposomal iron in pediatric patients with CKD and iron deficiency anemia (IDA).

METHODS: Cross-over study of 33 children with CKD and IDA was conducted. They were randomized into 2 groups (group A: 17 patients, group B: 16 patients) to receive either liposomal iron or IPC for 3 months. After an 8-week washout period, they were switched to the other therapy. Red cell and iron indices, as well as bone minerals and 25(OH)D₃, were measured at baseline and after each 3-month period. A follow-up visit was conducted at 4 weeks during the treatment period to report any possible adverse events.

RESULTS: Hb levels increased by at least 1 g/dL in 48% following liposomal iron therapy and 51.5% following IPC therapy. There was no statistically significant difference in ΔHb, ΔFe, ΔsTR (transferrin receptor), or ΔTSAT (transferrin saturation) levels between the groups (p > 0.05). By mixed model analysis, IPC showed a higher Hb and TSAT and lower TRresponse compared with liposomal iron. IPC, but not liposomal iron, led to a significant reduction in serum phosphorus in both groups. Thirty-six percent of IPC recipients experienced adverse effects, compared to 3% of liposomal iron recipients.

CONCLUSIONS: Both IPC and liposomal iron effectively improved iron status in children with CKD and IDA. However, IPC indicated a superior response, whereas liposomal iron was associated with a more favorable tolerability profile.

PMID:41540129 | DOI:10.1007/s00467-025-07138-w

Vet Res Commun. 2026 Jan 15;50(2):105. doi: 10.1007/s11259-025-11044-9.

ABSTRACT

BACKGROUND: Burkholderia pseudomallei, the causative agent of melioidosis in humans and animals, has been implicated in acute infections with high mortality rates in animal hosts and in mastitis in dairy cattle. It has intrinsic resistance to a wide range of antibiotics and is also known to possess a multitude of virulence determinants. This study provides baseline data on the occurrence of this pathogen in bovine milk samples in Osogbo, Southwestern Nigeria.

METHODS: A total of 371 milk samples collected from dairy cows with clinical and subclinical mastitis were assessed for the presence of B. pseudomallei using phenotypic microbiological techniques, confirmed by molecular methods. Selected resistance (folA, folP, Omp38, bpeE and bpeF) and virulence (bsaU, pili/fimbriae, bimA, tssA and wbiE) genes were screened for using self-designed specific primers, while antibiotic susceptibility testing against clinically relevant antibiotics was via the Kirby-Bauer disc diffusion technique.

RESULTS: Molecular identification confirmed 16 isolates (4.31%) as B. pseudomallei. Resistance to amoxicillin-clavulanic acid, imipenem, tetracycline and ceftazidime was absolute (100.0%), trailed by trimethoprim-sulfamethoxazole (SXT) at 93.8%. Meropenem exhibited the highest activity in vitro, as 93.8% of isolates were susceptible to it. All isolates (100.0%) were classified as extremely drug-resistant (XDR), with multiple antibiotic resistance indices ≥ 0.2. All isolates (100.0%) also harboured both resistance and virulence determinants, with 68.8% having ≥ 6 genes – 93.75% had the folP gene. The predominant virulence gene was BsaU, detected in 87.5% of isolates. No isolates had the wbiE gene.

CONCLUSION: The presence of XDR strains and carriage of multiple resistance and virulence genes in B. pseudomallei strains portend serious implications in affected dairy cattle. This study recommends prudent antibiotic use in dairy cattle and the proper processing of milk before consumption to limit the risk of zoonotic transmission.

PMID:41538090 | DOI:10.1007/s11259-025-11044-9

J Comput Neurosci. 2026 Jan 15. doi: 10.1007/s10827-025-00918-1. Online ahead of print.

ABSTRACT

Understanding how neurons encode multiple simultaneous stimuli is a fundamental question in neuroscience. We have previously introduced a novel theory of stochastic encoding patterns wherein a neuron’s spiking activity dynamically switches among its constituent single-stimulus activity patterns when presented with multiple stimuli (Groh et al., 2024). Here, we present an enhanced, comprehensive statistical testing framework for such “multiplexing”. As before, our approach evaluates whether dual-stimulus responses can be accounted for as mixtures of Poissons related to single-stimulus benchmarks. Our enhanced framework improves upon previous methods in two key ways. First, it introduces a stronger set of foils for multiplexing, including an “overreaching” category that captures overdispersed activity patterns unrelated to the single-stimulus benchmarks, reducing false detection of multiplexing. Second, it detects continuous mixtures, potentially indicating faster fluctuations – i.e. at sub-trial timescales – that would have been overlooked before. We utilize a Bayesian inference framework, considering the hypothesis with the highest posterior probability as the winner, and employ the predictive recursion marginal likelihood method for non-parametric estimation of the latent mixing distributions. Reanalysis of previous findings confirms the general observation of fluctuating activity and indicates that fluctuations may well occur on faster timescales than previously suggested. We further confirm that multiplexing is more prevalent for (a) combinations of face stimuli than for faces and non-face objects in the inferotemporal face patch system; and (b) distinct vs fused objects in the primary visual cortex.

PMID:41537936 | DOI:10.1007/s10827-025-00918-1

Eur Radiol. 2026 Jan 15. doi: 10.1007/s00330-025-12261-1. Online ahead of print.

ABSTRACT

OBJECTIVES: This study aims to develop a deep learning model to assist physicians in accurately classifying negative, equivocal, and positive β-amyloid (Aβ) deposition stages in Alzheimer’s disease (AD).

MATERIALS AND METHODS: 1327 subjects from two cohorts underwent [¹⁸F]Florbetapir PET and were grouped by Aβ deposition. A cascaded attention-guided vision transformer (CA-ViT) framework was proposed to extract biologically significant regional information for fine-grained classification. To evaluate clinical utility, we assessed the diagnostic performance of physicians with and without the assistance of our proposed method.

RESULTS: The CA-ViT model demonstrated outstanding cross-center performance, achieving accuracies of 82.8% [79.1%, 86.5%] (96% confidence interval, CI) and 78.0% [75.1%, 80.9%] in three-class classification tasks in the two cohorts, respectively. Our proposed model-assisted physicians exhibited significant improvements in diagnostic accuracy (from 56% to 66% and from 50% to 77%).

CONCLUSION: The CA-ViT model effectively decodes fine-grained pathological information from [¹⁸F]Florbetapir PET imaging, enabling accurate stratification of Aβ deposition to assist physicians in early monitoring of AD.

KEY POINTS: Question Deep learning has the potential to assist physicians in accurately classifying β-amyloid deposition stages in early Alzheimer’s disease. Findings The proposed diagnostic model is a promising computer-aided tool for early assessment of amyloid deposition and demonstrates improved physician performance. Clinical relevance Equivocal amyloid deposition often complicates early Alzheimer’s disease diagnosis and may delay optimal interventions. Our model, validated on PET scans from multiple centers, enhances the identification of these equivocal cases and improves diagnostic accuracy among less-experienced physicians.

PMID:41537783 | DOI:10.1007/s00330-025-12261-1

Eur Radiol. 2026 Jan 15. doi: 10.1007/s00330-025-12253-1. Online ahead of print.

ABSTRACT

OBJECTIVES: Besides clinical examination, cranial CT plays a critical role in diagnostics in neurosurgery. In trauma cases or perioperatively, having low-barrier access to CT-like imaging would be highly beneficial. Therefore, this feasibility study examines at an early stage if and how well synthetic cranial CT imaging can be generated from biplanar radiographs of adult neurosurgical patients using deep learning.

MATERIALS AND METHODS: Two 2D to 3D generative adversarial networks (GANs) were trained for the generation of synthetic cranial CTs using radiographs taken in two planes as input. Model 1 uses digitally reconstructed radiographs (DRRs) as input, while model 2 was trained using real X-rays. In total, model 1 was trained and validated using 235 images from three separate centers. Model 2 was trained and tested using 1323 images from a single center.

RESULTS: The performance of the model using DDRs as input reached a peak-signal-to-noise ratio (PSNR) of 15.61 and a structural similarity index measure (SSIM) of 0.782 during external validation. The second model, using real X-rays as input, attained a PSNR of 14.69 and an SSIM of 0.717 upon internal validation.

CONCLUSIONS: At the present stage, the synthetic cranial tomography scans generated as part of this study show promise but do not seamlessly correspond to ground-truth CTs. However, this proof-of-concept study is the first to derive such artificial cranial images using deep learning and can serve as a starting point for further investigation.

KEY POINTS: Question Cranial computed tomography involves radiation, logistical challenges, and access is limited in rural areas. Generating synthetic CT images with deep learning could address these challenges. Findings Two deep-learning models were trained to produce CT images from radiographs. Reconstruction from DRRs is promising, but using real X-rays remains more challenging. Clinical relevance As a proof-of-concept, the models’ exact clinical relevance remains to be defined. The proposed approach may broaden access to tomographic neuroimaging, reduce radiation, and enhance intraoperative and maybe even diagnostic support, potentially improving outcomes in neurosurgery and neuro-critical care.

PMID:41537782 | DOI:10.1007/s00330-025-12253-1

Eur Radiol. 2026 Jan 15. doi: 10.1007/s00330-025-12246-0. Online ahead of print.

ABSTRACT

OBJECTIVES: Pretreatment MRI was employed to develop and validate a combined model integrating clinical features with deep learning for rectal cancer.

MATERIALS AND METHODS: We retrospectively collected 458 patients from three hospitals and followed them up for at least 3 years. Clinical, pathological and imaging data were collected. Multi-instance learning (MIL) was used to integrate prediction across multiple slices to improve the performance of the model. To improve predictive performance, a nomogram combining deep learning features and clinicopathologic parameters was constructed. Model performance was assessed using Harrell’s C-index and time-dependent ROC curves.

RESULTS: The training set included 268 patients, 115 patients in the validation set and 75 patients in the external test set. For OS, the MIL model achieved a C-index of 0.757 in the training cohort, 0.754 in the validation cohort, and 0.741 in the test cohort, compared to 0.666, 0.772, and 0.756 for the clinical model, respectively. The combined model, which integrates MIL features with clinical features, further improved predictive performance, with C-index values for OS at 0.819, 0.822 and 0.759 and for DFS at 0.768, 0.750 and 0.721 across the training, validation and external test cohorts.

CONCLUSIONS: By leveraging the complementary strengths of clinical and deep learning approaches, the combined model enhances predictive robustness, enabling more accurate and personalized pretreatment risk assessment in rectal cancer.

KEY POINTS: Question Rectal cancer management requires more precise prognostic models to optimize treatment strategies and improve clinical decision-making for individual patients. Findings The combined model leverages synergistic effects between clinical and deep learning features, achieving enhanced prognostic performance and enabling more personalized pretreatment risk stratification. Critical relevance This study demonstrates that MIL extracts deep learning features complementary to clinical knowledge. The combined model leverages this synergy, providing clinicians with a more powerful tool for personalized prognostic assessment.

PMID:41537781 | DOI:10.1007/s00330-025-12246-0

Eur Radiol. 2026 Jan 15. doi: 10.1007/s00330-025-12180-1. Online ahead of print.

ABSTRACT

BACKGROUND: The CT-derived density of coronary artery calcification is increasingly associated with the risk of ischemic heart disease. Whether this principle also applies to intracranial artery calcifications (IAC) and cerebrovascular disease risk is unknown, primarily due to the lack of population-based estimates of IAC density and its determinants. We investigated these facets in this cohort study.

MATERIALS AND METHODS: In 2464 community-living individuals who underwent non-contrast CT, we measured IAC density and assessed its correlation with IAC volume using Spearman’s ρ. We described its distribution in intracranial carotid artery calcification (ICAC), with specific estimates for its subtypes, and vertebrobasilar artery calcification (VBAC). We investigated associations between risk factors and IAC density using multivariable ordinal regression models.

RESULTS: The prevalence of IAC was 82.8%, with a median density of 232 (IQR 189-287) HU. IAC density correlated moderately with volume (ρ 0.67, 95% CI [0.65-0.70]). ICAC was predominantly composed of higher density, with 80.1% of affected participants having components of ICAC above 400 HU, whereas only 32.0% of participants with VBAC had components above 400 HU. Intimal subtype ICACs showed a predominance for lower densities when compared to medial subtype ICACs. The main determinants of IAC density were hypertension, use of lipid-lowering medication, and smoking, with adjusted odds ratios of 1.59 [1.28-1.90], 1.55 [1.26-1.91], and 1.33 [1.10-1.61], respectively.

CONCLUSION: IAC density differs significantly between the anterior and posterior cerebropetal arteries. While IAC density correlated only moderately with its volume, the associations between cardiovascular risk factors and IAC density were mostly similar to those observed with IAC volume.

KEY POINTS: Question Drivers of the CT density of intracranial artery calcifications are unknown and may reveal novel risk targets for population-based prevention strategies. Findings Calcifications of the anterior cerebral circulation are denser than those of the posterior circulation. Hypertension, diabetes, and smoking are key drivers of calcification density, resembling most drivers of its volume. Clinical relevance Calcification density may serve in distinguishing subtypes of intracranial calcifications, improving detection of subtype-specific effects. Further research is warranted to determine the role of intracranial arteriosclerosis density in prevention strategies for cerebrovascular diseases.

PMID:41537780 | DOI:10.1007/s00330-025-12180-1

Nat Methods. 2026 Jan 15. doi: 10.1038/s41592-025-02923-9. Online ahead of print.

ABSTRACT

Microbial community analysis typically involves determining which microbial features are associated with properties such as environmental or health phenotypes. This task is impeded by data characteristics, including sparsity (technical or biological) and compositionality. Here we introduce MaAsLin 3 (microbiome multivariable associations with linear models) to simultaneously identify both abundance and prevalence relationships in microbiome studies with modern, potentially complex designs. MaAsLin 3 can newly account for compositionality either experimentally (for example, quantitative PCR or spike-ins) or computationally, and it expands the range of testable biological hypotheses and covariate types. On a variety of synthetic and real datasets, MaAsLin 3 outperformed state-of-the-art differential abundance methods, and when applied to the Inflammatory Bowel Disease Multi-omics Database, MaAsLin 3 corroborated previously reported associations, identifying 77% with feature prevalence rather than abundance. In summary, MaAsLin 3 enables researchers to identify microbiome associations more accurately and specifically, especially in complex datasets.

PMID:41540124 | DOI:10.1038/s41592-025-02923-9