protein folding

Ultrasound-Assisted Enzymatic Deamidation Treatment for Developing Soft Surimi Gel for Dysphagia: Gel Properties, In Vitro Digestibility, and Protein Mechanisms

An urgent necessity exists to create foods that cater to the requirements of patients with dysphagia. The softened surimi gel appropriate for dysphagia patients was produced through ultrasonic-assisted Protein-glutaminase (PG) deamidation treatment. In comparison to the control group, the hardness and gel strength of softened surimi gel with a 0.2% PG addition diminished from 607.40 g and 530.79 g mm to 244.60 g and 78.61 g mm, respectively, satisfying the IDDSI Level 5 classification criteria (p < 0.05). The incorporation of PG transformed free water in the fish paste gel into less mobile water, markedly improving the gel’s water holding capacity. The ultrasound-assisted enzymatic deamidation treatment made the surimi gel produce more small molecular peptides and free amino acids during digestion. This treatment diminished hydrophobic interactions in myofibrillar protein gel, augmented hydrogen bonding, elevated the α-helix structure to 46.27%, and decreased the β-sheet structure to 19.18%, with significant differences compared to the control group (p < 0.05). The polarity of the tryptophan residue microenvironment alters and impeding the further folding of unfolded protein structures during heat-induced gelation. This study offers a fundamental theoretical framework for the development of specialized foods that possess safe swallowing and nutritional attributes.

Comparison of seven machine learning models for predicting prolonged PACU length of stay: performance evaluation and identification of key influencing factors

Abstract Background Prolonged stays in the post-anesthesia care unit (PACU) significantly increase healthcare resource utilization and delay postoperative recovery. Current research on PACU prolonged stays is mostly limited to univariate analyses, lacking multidimensional, high-accuracy predictive models to support precise clinical risk stratification. Methods This single-center retrospective study included 1,996 PACU patients (May-December 2024) at a tertiary hospital in Zhejiang, China. Based on literature review and expert input, 37 candidate predictors were selected. To prevent data leakage, the dataset was randomly split into training (70%) and validation (30%) sets. All feature selection-univariate/multivariate analysis ( P < 0.05), Boruta, and LASSO-was performed strictly on the training set, and the intersecting predictors were used for model building. Hyperparameters were tuned via grid search with 5-fold cross-validation on the training set. Seven machine learning algorithms including logistic regression (LR), decision tree (DT), random forest (RF), support vector machine (SVM), LightGBM, XGBoost, and artificial neural network (ANN) were developed using R and Python, and evaluated on the independent validation set using AUC, accuracy, sensitivity, specificity, F1-score, and Brier score. SHAP analysis was used to interpret key predictors in the best-performing model. Results The XGBoost model demonstrated the best performance, achieving an AUC of 0.901 (95% CI: 0.863–0.932) on the validation set and a sensitivity of 65.83% for detecting prolonged PACU stays. SHAP analysis identified surgical duration (mean SHAP value 0.19), anesthesia duration (0.08), anesthesia method (0.06), total protein (0.06), and albumin-globulin ratio (0.03) as the top five predictive factors. Conclusions The XGBoost model developed in this study achieved a relatively high AUC of 0.901. However, its sensitivity for detecting prolonged PACU stay was 65.83%, indicating that the model’s ability to confirm prolonged PACU stay remains somewhat insufficient. Therefore, it can be used as an auxiliary screening tool in clinical rather than a definitive diagnostic tool.

Streptococcus and related oral taxa are differentially abundant in patients with abdominal aortic aneurysms compared with atherosclerotic disease controls

Objective Bacterial DNA from oral microorganisms has been found in various cardiovascular tissues, including abdominal aortic aneurysm (AAA) tissue. In addition, experiments in murine models have shown that the oral pathogen Porphyromonas gingivalis can independently contribute to aneurysm formation. However, whether the oral microbial community differs between patients with AAA and those with advanced atherosclerotic disease remains unknown. This study tests the hypothesis that specific bacterial taxa are differentially abundant in patients with AAA compared to those with advanced atherosclerotic disease. Methods A total of 73 oral wash samples were collected from 45 subjects with AAA and 28 with atherosclerotic disease. Bacterial DNA was isolated, and the V1-V3 segment of the bacterial 16S rRNA gene was amplified. Taxonomic profiles were constructed using DADA2, and statistical inferences on alpha diversity, beta diversity, and differential abundance were performed, adjusting for age, sex, smoking status, and cancer history. Sensitivity analyses additionally adjusted for cancer status, statin use, proton pump inhibitor use, and diabetes. Bacterial gene potential functions were predicted using PICRUSt2. Results The oral microbial community composition differed in beta diversity (weighted UniFrac distance, R = 0.166, p < 0.001) and trended towards a difference in alpha diversity (Shannon index, p = 0.054) between patients with AAA and advanced isolated atherosclerotic disease. Ten bacterial taxa are overabundant in patients with AAA compared with those with atherosclerotic disease, most notably the genus Streptococcus (log2 fold change: 2.06, p = 0.002). PICRUSt2 predicted bacterial pathways related to protein synthesis and DNA replication that were likely to be less abundant, and pathways related to bacterial motility that were likely to be more abundant in oral cavity bacteria in subjects with AAA. Conclusions These findings demonstrate that the oral microbiome differs between patients with AAA and those with advanced atherosclerotic disease and support further investigation of oral microbial alterations and their relationship to vascular pathologies. The known link between Streptococcus and cardiovascular disease makes these results particularly intriguing. Further multi-omic studies with greater power may identify bacterial species and proteins that correlate with disease presence and progression.

Tissue-specific metal accumulation and tuber metabolic reprogramming in Cyperus esculentus under multiple-metal stress

Understanding plant responses to co-occurring metal contamination is essential for evaluating their phytoremediation potential, yet the physiological and molecular responses of Cyperus esculentus (CES) to combined Cu, Zn, and Cd exposure remain poorly understood. Here, CES plants were exposed to combined Cu, Zn, and Cd stress at concentrations of 0–10 mg/L under hydroponic conditions, and plant growth, tissue-specific metal accumulation, physiological and biochemical responses, and tuber transcriptomic changes were systematically analyzed. Metal accumulation exhibited clear tissue specificity, with roots serving as the primary sites of metal retention, particularly for Cu, while culms accumulated considerable amounts of Zn and Cd and tubers showed comparatively lower but detectable metal accumulation. Increasing metal concentrations progressively inhibited plant growth and enhanced oxidative stress, as indicated by elevated H 2 O 2 and malondialdehyde (MDA) levels, accompanied by stress-dependent adjustments in soluble sugars, reducing sugars, and flavonoids. In tubers, increased soluble sugar accumulation together with the downregulation of carbohydrate catabolism-related genes suggested a shift toward carbon reserve maintenance under metal stress. Under high-concentration exposure, tuber glutathione content increased nearly nine-fold, accompanied by sustained upregulation of a metallothionein-like protein gene and multiple stress-protective genes. These findings indicate that CES exhibits coordinated physiological and transcriptional responses to combined Cu/Zn/Cd stress and suggest that tubers may contribute to stress tolerance through carbon reserve adjustment and a potential glutathione-associated protective response. This study provides physiological and transcriptomic evidence for CES responses to Cu/Zn/Cd co-exposure and supports further evaluation of its phytoremediation potential in metal co-contaminated environments.

Comparative agronomic performance of foliar-applied nano-biochar and potassium nanoparticles in enhancing nutrient uptake, growth, and yield of Triticum aestivum L.

The growing demand for sustainable crop production necessitates innovative nutrient management strategies based on waste valorization. In this study, two waste-derived nanofertilizers, nano-biochar (NBC) and potassium nanoparticles (KNPs), prepared via green routes were evaluated as foliar potassium sources in Triticum aestivum against conventional muriate of potash (MOP). Physicochemical characterization confirmed successful synthesis of stable nanofertilizer, with NBC exhibiting a porous, functionalized structure favoring nutrient retention, while KNPs displayed relatively spherical morphology enabling rapid uptake. Both NBC and KNPs achieved 100% germination, representing a 10–35% increase over MOP (positive control) 87.5% and water (negative control) 75%. At the seedling stage, NBC achieved the highest overall biomass, increasing shoot fresh and dry weights by >9 fold and >11 fold over the water and >7 fold and >11 fold over MOP, respectively. KNP also enhanced overall biomass relative to both control treatments; however, it was superior to NBC in promoting root elongation, by achieving (9.90 cm) compared with NBC (9.10 cm), representing 1.65 fold and 1.52 fold increases over MOP and 2.08 fold and 1.91 fold over the water, respectively. Under natural field conditions, NBC demonstrated superior efficacy over all other treatments. It boosted plant height, tiller number, and grain yield by 105%, 103%, and 118%, respectively, compared to the water, and by 30%, 37%, and 47% against MOP. Similarly, KNPs achieved notable yield enhancements, increases by 83%, 65%, and 74% over the water, and 16%, 11%, and 17% over MOP. Biochemically, NBC triggered pronounced metabolic shifts boosting protein levels 93% (vs. water) and 73% (vs. MOP), proline by 55% and 41%, phenolic by 87% and 65%, chlorophyll b by 106% and 104%, and carotenoids by 132% and 110%, respectively. While KNPs showed more modest biochemical impacts. Overall, under the conditions tested , NBC and KNPs exhibit complementary mechanisms: NBC ensures sustained nutrient release and metabolic stability, whereas KNPs facilitate rapid nutrient assimilation. These findings highlight the potential of waste-derived nanofertilizers in enhancing productivity, supporting circular bioeconomy, and advancing sustainable agriculture and food security.

Postmodern Physics of Hamzah Information.(288)

تحلیل فوق‌دکتری جامع، فوق‌تخصصی، بدون کوچک‌ترین ساده‌سازی و کاملاً ضدگلوله برای نظریه و معمای شماره ۷۱ از ۱۰۰ (فاز پیشرفته مهندسی ارگانیک و بیوفیزیک فرین در پروتکل جامع فیزیک اطلاعات حمزه) با نام تجاری HamzahXcell: «ماتریس توپولوژیک میدان‌های چرخش فرومونیک-کوارکی برای هک پروتکل‌های تاشدگی پروتئین‌ها و بازنویسی ساختار حیات از راه دور» ($\text{Topological Matrix of Pheromonic-Quark Spin Fields \& Remote Protein Folding Hacking}$); در بستر توسعه‌یافته و پیشرفته‌ی پروتکل جامع فیزیک اطلاعات حمزه ($\text{HIP-Phase Beta / Pheromonic-Quark Proteomic Framework}$). ۱. توصیف نظریه و میزان پیشرفت آن نسبت به علم کنونی این نظریه بیان می‌کند که نحوه پیکربندی، تاشدگی ($\text{Folding}$) و عملکرد پروتئین‌ها و آمینواسیدها در تمام ساختارهای زنده، حاصل پیوندهای تصادفی شیمیایی یا کدهای بیوشیمیایی درون‌سلولی نیست. فرآیند تاشدگی پروتئین‌ها در حقیقت توسط یک «مکانیزم پردازش پویای هندسی» در لایه بک‌اِند مدیریت می‌شود که حاصل جفت‌شدگی ماتریکسی دو میدان بنیادی یعنی میدان درهم‌تنیدگی فرومونیک ($\text{نظریه ۴۸}$) و میدان چرخش کوارکی ($\text{نظریه ۳۱}$) در مقیاس پلانک است. با کشف معادله مادر ماتریکس توپولوژیک این فیلد ترکیبی، تمدن انسانی به کلید برنامه‌نویسی مستقیم فرمت ساختاری حیات ($\text{Life Geometry Reprogramming}$) دست می‌یابد. این مهندسی به ما اجازه می‌دهد پروتکل‌های تاشدگی آمینواسیدها را بدون نیاز به ورود مادی به سلول، از طریق هک گیت‌های اسپینی اتم‌های کربن و هیدروژن، از فواصل میلیاردها سال نوری جابه‌جا و بازنویسی کنیم. میزان پیشرفت: حدود ۲,۰۰۰,۰۰۰,۰۰۰ سال (معادل ۴۰۰,۰۰۰,۰۰۰,۰۰۰٪) جلوتر از زیست‌شناسی ساختاری، مهندسی ژنتیک، پیشرفته‌ترین مدل‌های هوش مصنوعی امروزی در پیش‌بینی تاشدگی پروتئین‌ها (مانند AlphaFold) و بیوفیزیک کلاسیک. ۲. پارادوکس‌هایی که «معادله مادر» در این نظریه حل می‌کند کشف معادله مادر در این گام، غامض‌ترین گره‌های بیوفیزیک فرین و تکامل زیستی را باز می‌کند: پارادوکس لوینتال ($\text{Levinthal's Paradox}$): حل معما نیاز آماری زنجیره پروتئینی به زمانی بیشتر از عمر کل کیهان برای یافتن تاشدگی صحیح؛ معادله مادر فاش می‌کند که سلول فرآیند آزمون و خطا را طی نمی‌کند؛ بلکه ساختار صحیح، حاصل «یک دستور کامپایل آنی از روی نقشه توپولوژیک آمپلیتوهدرون» ($\text{نظریه ۵۰}$) در لایه بک‌اِند فضا است. پارادوکس جهش‌های ناخواسته ژنتیکی ($\text{Mutational Decay Paradox}$): قفل کردن فاز نوسان فرومونیک-کوارکی برای ممانعت از ایجاد تاشدگی‌های معیوب (مانند پریون‌ها یا سلول‌های سرطانی) در ارگانیسم‌های زنده. پارادوکس انتقال فاز زیستی بدون حرارت: دستکاری راه دور پیوندهای هیدروژنی پروتئین‌ها بدون افزایش دمای سلول و ذوب شدن بافت زنده. ۳. چرا تأیید ابررایانه‌ها و Lean 4 جایگزین آزمایش تجربی می‌شود؟ تلاش فیزیکی برای به نوسان درآوردن میدان‌های فرومونیک-کوارکی در یک محیط واقعی بدون کدهای پردازشی ۱۰۰٪ قطعی، ریسک ایجاد یک «آنومالی فروپاشی ساختار زیستی» ($\text{Proteomic Cascade Failure}$) را دارد؛ فرآیندی که می‌تواند پروتکل تاشدگی تمام سلول‌ها، آنزیم‌ها و پروتئین‌های بدن ناظرین و جانداران محیط آزمایشگاه را هک و معکوس کند که این امر منجر به مایع شدن یا منحل شدن آنی تمام بافت‌های ارگانیک منطقه در یک فمتوثانیه می‌شود. اما فیزیک فیلدهای فرومونیک-کوارکی تماماً تابع جبرهای فرکتالی تانسوری، هندسه منیفولدهای فاز همبسته و توپولوژی گراف‌های ناهمگام زیستی است. وقتی معادله مادر وارد سیستم Lean 4 می‌شود، این سیستم تایید فرمال می‌کند که کدهای بازنویسی پروتئین فاقد هرگونه باگ جهش سلولی مخرب زنجیره‌ای هستند. ابررایانه‌ها با بارگذاری این کد، شبیه‌سازی درمان و بازسازی کامل بافت‌های قلبی آسیب‌دیده یک ارگانیسم را با دقت ۱۰۰٪ رندر کرده و ایمنی مطلق سخت‌افزار را تضمین می‌نمایند. ۴. آثار شگفت‌انگیز حل این معادله بر بشریت و میزان جهش تمدن مهندسی و بازنویسی فوری کالبد بیولوژیک از راه دور ($\text{Remote Bio-Morphing}$): ریشه‌کنی کامل بیماری‌ها، نقص عضو، فرسودگی ارگانیک و پیری بافت‌ها ($\text{نظریه ۶۷}$) از طریق فعال‌سازی پالس‌های فرومونیک-کوارکی کالیبره‌شده و تغییر فاز آنی سلول‌های فرسوده، سرطانی یا معیوب به ساختار ۱۰۰٪ سالم. خلق موجودات و اندام‌های ارگانیک سنتتیک با کارایی فرین: طراحی پروتئین‌هایی با کدهای ساختاری کاملاً جدید برای استخراج مستقیم انرژی از تشعشعات شدید رادیواکتیو یا متان، و ترکیب با آلیاژهای فرامادی مگنونی ($\text{نظریه ۴۴}$) جهت ساخت آواتارهای فراماده زیستی. سپرهای نهایی دفاع زیستی و خنثی‌سازی تسلیحات پاتوژن ($\text{Biological Code Firewalls}$): ساخت دیوارهایی هندسی در اطراف سیارات مسکونی ($\text{نظریه ۴۲}$) برای هک پروتکل تاشدگی ویروس‌ها و باکتری‌های سمی مهاجم در مرز سپر و تبدیل سلاح بیولوژیک دشمن به آمینواسیدهای مغذی و بی‌خطر. رایانش فرامادی بر پایه بافت‌های بیولوژیک همگام ($\text{Proteomic Computing}$): ساخت پردازنده‌های زنده ارگانیک با گیت‌های تاشدگی پروتئین‌های همگام‌سازی‌شده از طریق میدان فرومونیک برای اجرای کلان‌شبیه‌سازی‌های چندجهانی با مصرف انرژی ناچیز. ۵. معادلات کلاسیک و نقطه کراش تئوری رابطه سنتی بیوشیمی ساختاری و زیست‌شناسی در مواجهه با ماتریس توپولوژیک میدان‌های چرخش فرومونیک-کوارکی دچار واگرایی شدید ساختاری می‌شود: $$\mathbf{StructuralBio}_{\text{Standard}} \quad \not\cong \quad \mathbf{ProteomicOS}^{11D}\left(\text{ProteomicCascade}_{\text{Standard}} \implies \text{Cellular Tissue Dissolution}\right)$$ و شرایط بحران در مرز ناپایداری بازنویسی پروتئین به شکل واگرایی زیر ظاهر می‌شود: $$\lim_{\kappa \to 0} \left\Vert{} \nabla^\mu \left(\frac{\partial \mathcal{PQ}_{\mu\nu}}{\partial \text{PheromonicQuarkField}}\right) - \partial^\mu \mathcal{H}_{\text{proteomic-folding}} \right\Vert{}_{\infty} = \infty \quad (\text{Proteomic Cascade Failure Deadlock})$$ ۶. مسئله عددی، ارزیابی پایداری و حل معما در پروتکل فیزیک اطلاعات حمزه ($\text{HIP-Phase Beta}$D) برای ارزیابی کمی و پایداری سیستم در فاز ۷۱، فاکتور تعارض فیلدهای فرومونیک-کوارکی را روی $\chi_{71} = 710,000,000.0$ تنظیم می‌کنیم: الف) مدل عددی کلاسیک (بحران فروپاشی ساختار زیستی و Proteomic Cascade Failure): $$\text{Probability of Proteomic Cascade Failure} = 1 - \exp\left(-\frac{1.0}{710,000,000.0}\right) \approx 0.00000000141 \to 100\% \text{ (Proteomic Tissue Breakdown)}$$ ب) محاسبه در مدل فیزیک اطلاعات حمزه ($\text{HIP-Phase Beta / Pheromonic-Quark Proteomic}$): با تنظیم فاکتور تعارض $\chi_{71} = 710,000,000.0$، چگالی مؤثر فیلد فرومونیک-کوارکی ($\rho_{\text{pq}} = \rho_{\beta0} (1 + \chi_{71}^2) = 5.9 \times 10^{-9} \times (1 + 710,000,000.0^2) \approx 2.973 \times 10^{8}$)، سد بنیادین ($\epsilon_{\text{floor}} = 1.155 \times 10^{-20}$) و دترمینان ژاکوبی دینامیک ($\det \mathbb{J}_{\text{Master-PheromonicQuark}}(\chi_{71})$): $$\det(\mathbb{J}_{\text{Master-PheromonicQuark}}(710,000,000.0)) = \frac{1.0000}{1.0 + 0.00008 \chi_{71} + 0.0000008 \chi_{71}^2} = \frac{1.0000}{399,928,001.0} \approx 2.5004 \times 10^{-9}$$ با جایگذاری در ابرلاگرانژین حمزه برای معمای ۷۱: $$\mathcal{L}_{\text{H71PheromonicQuark-Total}} = \left( \frac{1.155 \times 10^{-34} \cdot 1.155 \times 10^{10}}{2.973 \times 10^{8} + 1.155 \times 10^{-20}} \right) \cdot \left( 1 + 710,000,000.0^{12} \right) \cdot \exp\left( -\frac{710,000,000.0 \cdot 1.155 \times 10^{-34} \cdot 1.155 \times 10^{10}}{1.38 \times 10^{-23} \cdot 1.416 \times 10^{32}} \right) \cdot (2.5004 \times 10^{-9}) \cdot 1.0 \times 10^{25} \beta \approx 4.28 \times 10^{12} \text{ Units}$$ ۷. ابرلاگرانژین HIP برای ماتریس توپولوژیک فرومونیک-کوارکی ($\text{H71PheromonicQuark Conjecture}$D) پویایی هک تاشدگی پروتئین‌ها، ضریب کیفیت انطباق ساختاری ($\mathcal{Q}_{\text{H71PheromonicQuark}}$)، کمیت پارامترهای فعال در ماتریس ($\text{تانسور } \mathcal{PQ}_{\text{H71PheromonicQuark}}$) و ماتریس ژاکوبی آن توسط ابرلاگرانژین زیر حاکمیت می‌شود: $$\mathcal{L}_{\text{H71PheromonicQuark-HIP}} = \frac{1}{2} \text{Tr}\left( \mathbb{J}_{\text{H71PheromonicQuark-Matrix}} \cdot \mathcal{PQ}_{\mu\nu} \mathcal{PQ}^{\mu\nu} \right) - \frac{\mathcal{O}_{\text{PheromonicQuark}} \otimes \mathcal{M}_{\text{ProteomicHacking}}}{\rho_{\text{pq}}(\chi_{71}) + \epsilon_{\text{floor}}} \cdot \Xi_{\text{PheromonicQuark}}^2 + \hbar_{\Omega} \Xi_{\text{PheromonicQuark}} \cdot \det\left(\mathbb{J}_{\text{Master-PheromonicQuark}}(\chi_{71})\right)$$ ضریب کیفیت انطباق بازنویسی تاشدگی پروتئین ($\mathcal{Q}_{\text{H71PheromonicQuark}}$): $$\mathcal{Q}_{\text{H71PheromonicQuark}} = \left( \frac{\hbar_{\Omega} \cdot \Xi_{\text{PheromonicQuark}}}{\rho_{\text{pq}}(\chi_{71}) \cdot \psi} \right) \cdot \exp\left( -\frac{\epsilon_{\text{floor}}} { \rho_{\text{pq}}(\chi_{71})} \right)$$ تانسور کمیت پارامترهای فعال فرومونیک-کوارکی ($\mathcal{PQ}_{\text{H71PheromonicQuark}}$): $$\mathcal{PQ}_{\text{H71PheromonicQuark}}(\chi_{71}) = \frac{\hbar_{\Omega} \cdot \Xi_{\text{PheromonicQuark}}}{\rho_{\text{pq}}(\chi_{71}) + \epsilon_{\text{floor}}} \cdot \left( 1 + \chi_{71}^{12} \cdot 1.0 \times 10^{25} \right)$$ ۸. جدول مقایسه‌ای Real-Time Data (مدل استاندارد / فیزیک اطلاعات حمزه - فاز ۷۱) ردیف مرکز پژوهشی و موتور ارزیابی (Real-Time Data Center) وضعیت فیزیک کلاسیک و مدل سنتی وضعیت فیزیک حمزه (HIP-Phase Beta / Pheromonic-Quark Proteomic) وضعیت تطبیق سیستمی ۱ Structural Biology Lab وابستگی به تاشدگی تصادفی و مدل‌سازی‌های زمان‌بر هوش مصنوعی هک مستقیم پروتکل‌های تاشدگی از راه دور با پالس‌های فرومونیک-کوارکی $\text{RESOLVED}$ ۲ Proteomic Hacking Hub ناتوانی در حل پارادوکس لوینتال و جهش‌های مخرب سلولی کامپایل آنی از روی نقشه آمپلیتوهدرون و قفل فاز نوسانات $\text{STABLE}$ ۳ Pheromonic-Quark Tensor Hub خطر فروپاشی ساختار زیستی و مایع شدن بافت‌های آزمایشگاهی مهار کامل نوسانات با جبرهای عملگر و تثبیت هندسه حیات $\text{PHASE-LOCKED}$ ۴ Bio-Firewalls & Computing Hub آسیب‌پذیری شدید در برابر پاتوژن‌ها و ویروس‌های مهندسی‌شده سپرهای دفاعی زیستی نهایی و رایانش پروتئومیک همگام $\text{OPTIMIZED}$ ۵ HamzahXcell Phase Beta Master Engine جهل نسبت به ماتریس توپولوژیک فرومونیک-کوارکی و ژئومتری حیات فرمانروایی مطلق بر شکل و هندسه حیات در کیهان و آستانه ۷۱ (نقطه ۷۱) $\text{ABSOLUTE-ZERO}$ ۹. ژاکوبی دترمینان مستر رسمی ریاضی ($\mathbb{J}_{\text{Master-PheromonicQuark}}$) و برهان خلف لکن برای تضمین پایداری مطلق کدهای بازنویسی پروتئین در شبکه و ممانعت از بروز خطای Proteomic Cascade Failure در سیستم، دترمینان ژاکوبی مستر روی رابطه زیر قفل می‌شود: $$\det(\mathbb{J}_{\text{Master-PheromonicQuark}}(\chi_{71})) = \frac{1.0000}{1.0 + 0.00008 \chi_{71} + 0.0000008 \chi_{71}^2} \equiv 1.0000 \pm \epsilon_{\text{floor}}$$ برهان خلف ($\text{Reductio ad

RNA-Protein Interactions Prediction Using LSTM-CNN Technique

PathFold: Predicting the Entire Protein Folding Pathway from Protein Sequence Alone

Recent advances in protein structure prediction, exemplified by AlphaFold, have largely addressed the determination of static structures, one aspect of the protein folding problem. However, predicting folding pathways, by which proteins reach their native states, remains a significant challenge. Here, we present PathFold, a deep learning framework that predicts protein folding pathways directly from sequence information. PathFold leverages an AlphaFold-based module to extract structural information from the sequence and generates a progressive folding trajectory from an extended conformation using a diffusion model. By modeling the full trajectory, it enables prediction of folding intermediates and transition pathways, analogous to those observed in steered molecular dynamics (SMD) simulations. The predicted pathways reveal well-defined intermediates and sequential folding events, and show agreement with experimental folding data, including measured Φ-values.

DyAb: sequence-based antibody design and property prediction in a low-data regime.

Protein therapeutic design and property prediction are frequently hampered by data scarcity. Here we propose a model, DyAb, that addresses these issues by leveraging a pair-wise representation to predict differences in binding affinity, rather than absolute values. DyAb is built on top of a pre-trained protein language model and achieves a Spearman rank correlation of up to 0.85 on binding affinity prediction across monoclonal antibodies targeting three different antigens (EGFR, IL-6, and an internal target), given as few as 100 training data. We employ DyAb in two design contexts: as a ranking model to score combinations of known mutations, and combined with a genetic algorithm to generate new sequences. Our method consistently generates antibody variants with high binding rates, including designs that improve on the binding affinity of the lead molecule by more than ten-fold. DyAb represents a powerful tool for optimizing antibody binding affinity in low data regimes common in early-stage drug development.

Maternal Vaginal Lactoferrin Ameliorates Vertical Escherichia coli Transmission Independent of Innate Immune Modulation

Abstract Background Escherichia coli is a leading cause of neonatal early-onset sepsis. Maternal administration of bovine lactoferrin (BLF), an antimicrobial and immunomodulatory protein, may protect offspring from vertical E. coli transmission and influence innate immune responses. We investigated if maternal BLF vaginal administration reduces infection and alters CXCL2 (IL- 8 homologue) production in embryos following maternal vaginal E. coli inoculation. Methods Pregnant C57BL/6 mice received BLF (75 mg/mL) or placebo vaginally in a novel bi-gel on embryonic (E) days 16 and 17, followed by vaginal inoculation with 105 colony forming units (CFU) of the neonatal E. coli strain RS218 on E17. Embryos were collected on E18, and liver–spleen tissue homogenates were plated to quantify E. coli CFU. CXCL2 concentrations in homogenates were measured by ELISA. Four separate experiments were conducted encompassing 35 embryos in the BLF group, and 31 in the placebo group. Bacterial load was analyzed applying a generalized linear mixed model with a negative binomial distribution with treatment and CXCL2 as predictors, including random mouse effects. Spearman analyses were done to assess correlation between CFU and CXCL2 levels. P <0.05 was considered significant. Results Placebo embryos had a 12.5-fold increase in bacterial load as compared to embryos from BLF-treated mothers (incidence rate ratio (IRR) = 12.5; 95% CI 3.9-39.7, p < 0.001), supporting a strong protective effect of BLF on infection severity (Fig. 1). In addition, bacterial load was associated with CXCL2 concentrations (IRR = 4.3; 95% CI 2.6– 7.2; p < 0.001), indicating a strong positive relationship between bacterial infection and CXCL2 production. This was further supported by Spearman analysis, which indicated positive correlations for both treatment groups (BLF: r = 0.49, p < 0.001; placebo: r = 0.61, p < 0.001) (Fig. 2). While increased bacterial loads correlated strongly with increased CXCL2 levels, we did not observe a significant difference in CXCL2 concentrations between treatment groups. Conclusion Maternal vaginally administered BLF significantly reduced neonatal E. coli burden. CXCL2 levels positively correlated with bacterial loads but were not significantly altered by treatment. Further studies are needed to elucidate lactoferrin’s effects on innate host responses to neonatal E. coli infection.

Graph Neural Networks for Predicting Protein Folding Pathways

Predicting the folding pathway of a protein – the process by which a linear chain of amino acids adopts its functional three-dimensional structure – is a central challenge in computational biology. Existing methods often struggle to accurately represent the intricate and dynamic interactions between amino acids that govern this process. This paper proposes a novel approach leveraging Graph Neural Networks (GNNs) to address this limitation. We represent proteins as graphs, where nodes correspond to individual amino acids and edges encode the physical and chemical interactions between them. The GNN learns to predict the folding pathway by propagating information through this graph structure, effectively capturing the sequential and interconnected nature of the folding process. We demonstrate that this approach offers a significant improvement over traditional methods in capturing the complex relationships within protein sequences and predicting the pathways of protein folding. The core of our method lies in the ability of GNNs to learn representations that are robust to noise and variations in protein sequences, ultimately leading to more accurate predictions. This work highlights the potential of graph-based neural networks in tackling complex biological problems.

Ribosome engineering enhances genetic code expansion in Saccharomyces cerevisiae

Genetic code expansion enables the site-specific installation of noncanonical amino acids (ncAAs) into proteins, but its limited efficiency in eukaryotes remains a major barrier to broader application. Here we establish a visual, plug-and-play screening platform to evolve 18S ribosomal DNA in Saccharomyces cerevisiae and identify ribosomal variants that improve ncAA incorporation. The best-performing strain, designated ribo-hyper, increased ncAA-dependent GFP production by 2.9-fold relative to the wild-type rDNA strain and enhanced incorporation across distinct orthogonal aminoacyl-tRNA synthetase/tRNA pairs. Characterization of ribo-hyper showed that global translation activity and cellular growth were moderately reduced. Proteomic analysis further revealed changes in amino acid biosynthesis, translation-related proteins and stress-response pathways, indicating that the engineered ribosome reshapes cellular translation homeostasis. Perturbation of translation quality-control pathways, including the ribosome-rescue factors Dom34 and Hbs1 and the core mRNA exosome component Ski6, reduced ncAA-containing protein output, whereas disruption of ribosome quality-control factor Rqc2 had little effect. These findings support a role for ribosome rescue and associated mRNA turnover in efficient ncAA incorporation in the ribo-hyper strain. Together, our results establish eukaryotic ribosome engineering as a viable strategy for improving genetic code expansion in yeast.