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Health risk assessment of exposure to Polycyclic Aromatic Hydrocarbons in bread from Iranian markets: Application of monte carlo simulation approach

Background Polycyclic aromatic hydrocarbons (PAHs) are persistent environmental contaminants formed during high-temperature processing of food and are associated with both carcinogenic and non-carcinogenic health effects. Methods A total of 96 bread samples (Lavash, Sangak, and Barbari) were collected from industrial and traditional bakeries across eight municipal zones of Mashhad, Iran. Sixteen priority PAHs were quantified by GC-MS after microwave-assisted saponification and DLLME clean-up. Toxic equivalent concentrations (BaPeq), daily dietary exposure, incremental lifetime cancer risk (ILCR), and Monte Carlo probabilistic modelling (100,000 iterations) were used for risk characterisation. Results Mean Σ₁₆PAHs were significantly higher in traditional breads (68.63 ± 4.57 µg/kg) than industrial breads (39.87 ± 3.68 µg/kg; p < 0.001), with Lavash showing the highest levels (66.58 ± 6.02 µg/kg) and Sangak the lowest (40.95 ± 1.63 µg/kg). Naphthalene dominated light PAHs; fluoranthene and pyrene were predominant heavy PAHs. Mean BaP concentrations (0.05–0.10 µg/kg) were below the EU limit of 1 µg/kg. Mean TEQ was 0.18 µg/kg, with BaP contributing ~58%. Probabilistic assessment showed acceptable to low carcinogenic risk, slightly higher for traditional baking. Sensitivity analysis identified BaP as the primary driver. Compared globally, PAH levels in these Iranian breads indicate an acceptable risk, though continued monitoring and regulation are needed. Conclusion Current PAH exposure via bread in Mashhad is generally acceptable, but traditional baking markedly increases contamination. Transition to indirect-heating ovens, improved ventilation, and regular monitoring are recommended to minimise exposure.

High-gain and low-profile antenna using novel reflective surface for satellite applications

A low-profile and compact antenna that offers wideband and high-gain features for satellite applications is investigated in this work. Instead of using an AMC surface for enhancing gain as in conventional antenna designs, this paper proposes a reflective surface made of pure copper laminate, and the results exhibit a 5 dB gain enhancement. In addition, the broadband performance is realized by loading partial ground technique. As a result, only four patch elements, the antenna designed at the central frequency of 4.3 GHz exhibits a bandwidth of 21.4% at −10 dB, a peak gain of 14 dBi and an efficiency over 80% over the frequency range of operating. The design concept is validated through measurements conducted on a fabricated prototype. In addition, the performance of the proposed antenna is compared with existing literature in order to highlight the importance of the proposed work.

A fine-grained evaluation framework for urban land cover change based on feature monitoring with remotely sensed imagery

Against the backdrop of accelerating global climate change and urbanization, urban land cover change has emerged as a critical indicator for understanding the dynamic evolution of cities and the transformation of urban ecosystems. This study proposes a data-driven framework for fine-scale urban land cover change assessment based on the UASFNet model, enabling high-precision evaluation of urban land cover dynamics. The approach first performs preprocessing and co-registration of bi-temporal remote sensing images from the study area, and applies the trained UASFNet model to identify urban land cover types and extract land cover information for each temporal phase. The Analytic Hierarchy Process (AHP) is then employed to determine the weights of various indicator factors. By integrating building disturbance, greenbelt disturbance, and road disturbance indices, the framework quantitatively evaluates the intensity of land cover change at both pixel and regional scales. Experimental results across three benchmark datasets, consisting of high-resolution sub-meter RGB urban remote sensing imagery, demonstrate that UASFNet achieves superior segmentation accuracy, with mean Intersection over Union (mIoU) values of 91.52%, 93.31%, and 88.90%, substantially outperforming several state-of-the-art baseline models. Spatial analysis of the Langfang urban area (2017–2023) reveals a marked increase in impervious surface coverage (+16.86%) and a sharp decline in greenbelt (−40%), with the urban landscape exhibiting a multi-core, belt-like expansion pattern oriented toward newly developed districts. The proposed framework not only enhances the interpretability and generalization of remote sensing models in complex urban environments but also provides a scalable analytical tool to support urban spatial planning, ecological conservation, and sustainable city governance.

Analysis of EGFR signaling pathway; miRNAs and inflammatory biomarkers in a high-risk oral cancer population in Pakistan - An exploratory study

Oral cancer has high morbidity rates in the Asian region, specifically Pakistan. However, little insight is available regarding the molecular pathogenesis and inflammatory biomarkers of this preventable cancer. This study determined the association of EGFR, NFκB, COX-2, and miRNAs expression with the chewing habits and high-risk human papillomavirus (HR-HPV) infection in oral cancer patients. Formalin-fixed paraffin-embedded blocks (FFPE) (n = 50) were analyzed for transcriptional expression of EGFR, NFκB, and COX-2 by qPCR and COX-2 protein expression was checked by immunohistochemistry (IHC). Array profiling of ~2500 miRNAs was performed on nine samples, and five miRNAs were selected for validation from this profiling data. Appropriate statistical tests were applied to check the association of EGFR., NFκB, COX-2, and miRNAs with chewing and HR-HPV status, (p < 0.05 and 95% CI). Of the 50 samples, transcriptional expression of EGFR was observed in 13 (26%), NFκB in 11 (22%), and COX-2 in 17 (34%) samples, and the majority were chewers. EGFR and COX-2 expression was significantly associated with chewing gutka (the most carcinogenic form of chewing substance). A total of 281 miRNAs were dysregulated in miRNA profiling, and in validation, miR-222-3p was significantly downregulated in chewers expressing EGFR, NFκB, and COX-2 compared to non-chewers (p < 0.05). HR-HPV positivity was not correlated with miRNAs. This study suggests a significant association of chewing habits with EGFR and COX-2 expression. In addition, the molecular pathogenesis of OSCC suggests the substantial interplay of NFκB, COX-2, EGFR and miRNAs in chronic chewers, irrespective of HR-HPV involvement.

Sequencing of the invasive E. coli strain BEN2908 isolated from poultry: A comparative investigation of genomic regions shared with intestinal and extraintestinal model E. coli strains

Extraintestinal pathogenic Escherichia coli (ExPEC) cause disease outside the gut and include avian pathogenic E. coli (APEC), a leading cause of bacterial infections in poultry. Among their highly diverse types, strain BEN2908 stands out for its significant invasive ability across various human and avian cell types. Aiming to investigate further aspects of this strain and its plasmid, we sequenced and assembled the complete genome of BEN2908 and compared it to 22 E. coli strains, including other invasive strains such as adherent and invasive E. coli (AIEC) LF82 and NRG857c, by constructing a phylogenetic tree and using web-based characterization software. With these results, we selected eight strains closely related to BEN2908 to perform a ring comparison, including two APEC (APEC O1 and IMT5155), two neonatal meningitis E. coli (NMEC; RS218 and IHE3034), two uropathogenic E. coli (UPEC; 78-Pyelo and CFT073), one commensal E. coli (MG1655) and one adherent-invasive E. coli (AIEC; LF82). This revealed 20 genomic regions (GRs) of interest which were then analysed by CD-Search, BLASTp and KEGG Pathway databases. Many of the genes in these GRs had no previous description but showed similarity to known genes involved in sugar uptake, nitrogen metabolism, and dicarboxylate transport and processing, among other functions. These results were tabulated and used to infer possible pathways that could be involved in ExPEC pathogenesis, highlighting candidate genes that have been overlooked in ExPEC research.

Moisture as a key factor alleviating low-temperature stress: Effects of hydrothermal conditions on maize emergence

Early spring sowing of maize in semi-arid, wind-eroded regions is increasingly threatened by cold snaps due to climate change.These events, often coupled with uneven soil moisture distribution,compromise seedling emergence and early development. Identifying critical temperature and moisture thresholds is essential to ensure successful germination in these vulnerable environments.A factorial experiment was conducted in a controlled environment using maize seeds (Zea mays L.) exposed to diurnal temperature cycles.Treatments included five minimum temperatures (0,2,4,6,8°C), three chilling durations (2,4,6 hours),and four soil moisture levels (60,70,80,90% field capacity). Key germination metrics,including final germination rate, weighted germination time,synchrony,delay days,and seedling dry matter at day 30,were measured and analyzed using three-way ANOVA and Pearson correlations. Temperatures below 6°C significantly delayed germination and reduced final germination rates,particularly under low moisture conditions.Moisture levels ≥80% effectively mitigated chilling effects at moderate temperatures(4 ~ 6°C).Extended chilling durations further suppressed germination.The strongest interaction was observed between minimum temperature and soil moisture.Seedling dry matter accumulation was also significantly affected by all three factors and their interactions.Soil moisture serves as a critical buffer against chilling stress during maize germination. This study provides quantitative benchmarks for temperature and moisture combinations that optimize early maize emergence under extreme spring weather, offering practical insights for precision moisture management in semi-arid agriculture.

Trustworthy AI for medical decisions: Adversarially robust and fair machine learning prediction for Parkinson’s disease

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms, including tremor, rigidity, and postural instability. Machine learning (ML) models have shown promise for the diagnosis of PD; however, many existing approaches do not explicitly address fairness and robustness. As a result, these models can lead to biased outcomes across demographic groups and vulnerability to adversarial attacks. In this study, we used the Parkinson’s Progression Markers Initiative (PPMI) cohort, which includes clinical and demographic information from 1,084 participants spanning diverse age, sex, and racial groups. Our study addresses the key challenge of developing robust and equitable ML models to diagnose the progression of PD. We evaluated the performance of two fairness-optimized classifiers, namely, Random Forest (RF) and Decision Tree (DT). To evaluate model vulnerability, we applied adversarial techniques, specifically label leakage and data poisoning attacks, which simulate intentional or erroneous data alterations that can amplify biases and degrade accuracy. These adversarial manipulations substantially degraded model performance; specifically, DT accuracy declined by more than 10% between sensitive groups. The accuracy of the RF model decreased by 20%. Moreover, under attack, fairness metrics such as Statistical Parity Difference (SPD), which looks at differences in the chances of getting a positive prediction across demographic groups, and Equal Opportunity Difference (EOD) for differences in true positive rates between groups, both showed a decline. This pattern suggests that adversarial perturbations increased bias and widened performance disparities across demographic groups. Our results demonstrated that adversarial attacks increased the incidence of false positives and false negatives, thereby lowering the accuracy and fairness of the PD diagnostic predictions. These findings underscore the urgent need for robust and fairness-aware defenses in medical AI to mitigate racial, age, and gender disparities and ensure a reliable clinical decision-making process.

Effect of digital chemotherapy extravasation education based on Orem’s self‐care theory in cancer patients: A quasi-experimental study

Purpose Digital health education is crucial in preventing and managing chemotherapy extravasation among cancer patients. We aimed to evaluate the effectiveness of a digital chemotherapy extravasation educational intervention on knowledge, self-care abilities, and self-efficacy in cancer patients. Methods A quasi-experimental longitudinal design was conducted with 80 cancer patients at Hue University of Medicine and Pharmacy Hospital, Vietnam, from September 2022 to November 2023. The control group (n = 40) received routine health education on chemotherapy extravasation, while the experimental group (n = 40) participated in an extravasation education program based on Orem’s Self-Care Theory and utilized digital technology. This study assessed knowledge levels, self-care abilities, self-efficacy in preventing and managing chemotherapy extravasation, and the effectiveness of digital technology applications at three different time points. Chi-square tests, independent t-tests, and Analysis of Variance with Repeated Measures were employed to analyze the data. Results There were statistically significant differences in knowledge (F = 16.24, p  < 0.001), self-care abilities (F = 35.37, p  < 0.001), and self-efficacy (F = 46.27, p  < 0.001) between the intervention and control groups over time, with the intervention group showing greater improvements across all outcomes. Qualitative findings revealed three key categories: moving forward to self-care readiness, promoting emotional resilience, and enhancing a positive care environment. Patients perceived the digital technology applications as highly effective, especially in terms of perceived usefulness and ease of use, and showed significant changes across time (F = 9.93, p  < 0.001). Conclusion Digital chemotherapy extravasation education based on Orem’s Self-Care Theory offers an effective approach that enhances cancer patients’ knowledge while strengthening their self-care. Therefore, healthcare providers should integrate digital education into routine patient care to enhance self-efficacy, reduce complications, and support better treatment outcomes.

Dual‐Synergistic S‐Scheme Heterojunction for Targeting Dibenzylamine via Cascade Photoredox Catalysis of Benzylamine

Abstract Photocatalysis integrates oxidation and reduction processes, offering significant potential for driving synthetic transformations under mild conditions. However, the development of light‐driven redox cascade reactions—essential for expanding reaction diversity—remains challenging and unexplored. In this study, a semiconductor cluster‐based S‐scheme heterojunction, T4‐CdCu/ZIS , is designed for the photocatalytic redox cascade conversion of benzylamine (BA) to dibenzylamine (DBA) under a CO 2 atmosphere. The heterojunction operates via a dual internal‐external synergy. Internally, Fermi level‐engineered synergy enhances charge transfer kinetics, enabling rapid carrier separation and suppressed recombination while preserving favorable redox potentials. Externally, this internal synergy drives the tandem oxidation of BA to N ‐benzylbenzaldimine (BBAD) and its subsequent reduction to DBA, facilitated by a tight interface and highly exposed clusters. Consequently, exceptional photocatalytic BA conversion is achieved, yielding a record production rate of 8.31 mmol·g −1 ·h −1 with complete DBA selectivity. Mechanistic investigations suggest that BA undergoes dehydrocoupling on oxidative ZIS to form the BBAD intermediate, which then migrates to T4‐CdCu for reduction to DBA via adsorbed H species. As proof‐of‐concept, a drug, Cinacalcet , exemplifies the synthesis potential of secondary amines. This work provides a sustainable route for synthesizing secondary amines and inspires the design of advanced heterojunction catalysts for efficient redox cascade reactions.

Investigating traditional and novel predictors of a single versus multiple fragility fractures in a large observational cohort

Background Osteoporosis is a systemic skeletal disease characterised by reduced bone mass and a distortion of bone microarchitecture. It is clinically problematic as it leads to fragility fractures which confers excess morbidity and mortality on patients. Up to 32% of individuals will experience recurrent fragility fractures within two years of an initial fracture, yet existing risk models focus on the risk of having a single fragility fracture at a time. We aim to identify predictors of multiple fragility fractures to help improve risk stratification. Methods 43,801 patients referred for their first DXA scan in the northwest of England between June 2004 and February 2024 were analysed. Participants underwent lumbar spine and femoral scans to assess bone density and regional body composition. A generalized additive model reporting odds ratios was used to compare risk factors for a single versus multiple fragility fractures. Results Of the referred population, 14,212 (32.4%) had a single fragility fracture and 3,731 (8.5%) had multiple. Female gender was associated with lower odds of multiple fractures (OR 0.88, 95% CI: 0.79–0.99), while increased odds were linked to family history of fractures (OR 1.22, 95% CI: 1.11, 1.35), secondary osteoporosis (OR 1.15, 95% CI: 1.05, 1.26), rheumatoid arthritis (OR = 1.29, 95% CI: 1.08, 1.53), glucocorticoid therapy (OR = 1.18, 95% CI: 1.00, 1.39), smoking (OR 1.27, 95% CI: 1.12, 1.45) and falls risk (OR 2.02, 95% CI: 1.54, 2.63). The combination of falls risk and alcohol consumption increased multiple fracture odds (OR 7.62, 95% CI: 2.77, 20.94). Left femoral T-score and body fat percentage showed significant non-linear effects (both p < 0.001). Conclusion Multiple fragility fractures were associated with many traditional risk factors. We also identified a novel link between falls risk and alcohol consumption, as well as the significant associations with body composition.

Exploring sleep in farmed ungulates: A scoping review protocol

Sleep is essential to the life of all animals and has been broadly studied in humans and laboratory animals, although less explored in other species. Sleep may influence various aspects of an animal’s welfare, including their health, behaviour and mental states. Some researchers have begun to investigate sleep in farmed ungulates, including the role that sleep may play in animal welfare. This protocol aims to outline the methods of a scoping review aimed at describing sleep studies in farmed ungulates. The proposed scoping review aims to 1) map existing studies on sleep in farmed ungulates, 2) explore the animal welfare implications of this sleep research, and 3) identify knowledge gaps in need of future research. The PRISMA-P guidelines extension for scoping reviews will be followed, and the data will be made publicly available at the time of publication. A search will be performed in three databases: CAB Abstracts, PubMed, and Scopus. Articles will be screened using Covidence software and included if they measured sleep in species of farmed ungulates. The results will be reported using a descriptive synthesis of published sleep studies in farmed ungulates, including a timeline of when studies were published, the species and other characteristics of animals included, methods used to estimate sleep, and how sleep related to animal welfare outcomes.

Retraction: FSM-F: Finite State Machine Based Framework for Denial of Service and Intrusion Detection in MANET

Clinical value of Lipoprotein(a) combined with CatLet coronary score in predicting adverse events after emergency PCI for AMI patients

Background and aims Lipoprotein(a) [Lp(a)] promotes atherosclerotic plaque vulnerability through pro-inflammatory and thrombogenic pathways, while the CatLet© angiographic score quantifies coronary lesion complexity. We hypothesized that their integration would improve prognostication in acute myocardial infarction (AMI) after emergency percutaneous coronary intervention (ePCI). Methods In this retrospective cohort, 307 AMI patients undergoing successful ePCI (2020–2022) were stratified by 1-year major adverse cardiovascular/cerebrovascular events (MACCE). Serum Lp(a) and troponin I were measured post-admission. CatLet© and Gensini scores were assessed by blinded analysts. Multivariable logistic regression and ROC analyses evaluated predictive performance. Results MACCE patients (n = 78) exhibited higher Lp(a) (135.99 ± 33.07vs. 123.35 ± 42.70nmol/L, P = 0.0178) and CatLet© scores (33.58 ± 9.04vs. 30.80 ± 8.24, P = 0.0012) versus controls. Lp(a) (OR=2.339,95%CI:1.519–3.603, P <  0.001) and CatLet© score (OR=1.092, 95%CI:1.027–1.161, P = 0.005) independently predicted MACCE. The combined model Lp(a)≥70.70 nmol/L + CatLet© ≥ 18.6) significantly outperformed individual markers (AUC 0.862 [95%CI:0.83–0.96] vs. 0.780/0.833; DeLong’s test confirmed the superiority of the combined model over individual predictors (P = 0.0089, Z = 2.64 vs. Lp(a); P = 0.034, Z = 2.12 vs. CatLet© score), with 88% sensitivity and 83% specificity. Conclusions The Lp(a)-CatLet© synergy enhances MACCE risk stratification in ePCI-treated AMI, reflecting complementary pathobiological (Lp(a)-driven plaque vulnerability) and anatomical (CatLet©-quantified complexity) pathways. This dual-parameter approach could support post-PCI risk stratification and follow-up planning.

Exploring unintended pregnancy journeys among women with psychiatric vulnerability using interpretative phenomenological analysis

Background It is known that women with unintended pregnancies (UPs) experience many challenges. Women with psychiatric vulnerability may face specific concerns regarding the transmission of psychiatric vulnerability, parenting skills and bonding capacities with their offspring. This study aimed to explore how women with psychiatric vulnerability experience UPs. Methods This is a prospective qualitative study using semi-structured interviews during pregnancy and after delivery regarding the experiences of women with UPs and psychiatric vulnerability and involved partners. Follow-up interviews were conducted three to six months after delivery. Interpretative phenomenological analysis was employed to analyze the data. Results Women with psychiatric vulnerabilities described unintended pregnancies as complex events, often marked by ambivalent pregnancy intentions, concerns about generational trauma, and fears about parental adequacy. The pregnancies triggered heightened psychiatric symptoms, resurfacing childhood memories, and concerns about stigma, yet also motivated participants to seek support from mental health professionals and trusted others. Women adopted coping strategies such as focusing on the future, seeking distraction, and accepting support to manage emotional challenges. Across pregnancy and postpartum, many participants reported developing strong prenatal and postnatal bonding with the newborn, creating safety nets, and making intentional behavioral changes to support their babies. For several women, the unintended pregnancy ultimately fostered personal growth and contributed to an improvement in mental well-being. Conclusions This study elucidates the experiences of unintended pregnancies in women with psychiatric vulnerability. Our findings show that for women with psychiatric vulnerability, UPs may also become a window of opportunity for treatment, personal growth and create a safety net for the baby and oneself. This work may help mental healthcare providers to support comprehensively expectant parents who decide to continue UPs.

Molecular validation of carnivore scat surveys: Effects of climate, scat age, and observer experience on identification success

Non-invasive genetic sampling has become an essential tool for monitoring carnivores; however, the success of molecular identifications from scats varies widely across taxa, environments, and observers. Field-based assignments are also prone to misclassification, particularly when species are sympatric and produce similar scats. Understanding the determinants of molecular success and the concordance between field and genetic identifications is therefore critical for designing reliable surveys. We analysed 2,073 carnivore scats collected across five protected areas in Spain. A binomial GLMM showed that scat age, climatic conditions, observer identity, and year significantly influenced the probability of successful genetic identification. Fresh scats had nearly double the odds of yielding a valid genetic result compared with medium-aged scats, whereas higher precipitation and temperatures reduced success. Observer differences were also evident, and interannual variation suggested the presence of additional environmental effects. Of the 1,835 scats successfully identified, the overall match rate between field and molecular assignments was 75.9% (Cohen’s κ = 0.68), increasing to 80.0% (κ = 0.74) when genus-rank identifications ( Canis sp., Felis sp.) were considered correct. High-confidence field identifications achieved>90% agreement, but congeneric species, such as Martes foina and M. martes , were frequently misclassified. Our findings demonstrate that the interaction of environmental and human factors influences the success of identification. Field-based identifications, although often reliable, can lead to systematic biases in species-rich assemblages. We recommend incorporating molecular validation into carnivore surveys whenever species-level precision is required, especially for rare or threatened taxa.

Deep locomotion prediction learning over biosensors, ambient sensors, and computer vision

Innovative technologies for developing intelligent systems related to locomotion prediction learning are crucial in today’s world. Human locomotion involves various complex concepts that must be addressed to enable accurate prediction through learning mechanisms. Our proposed system focuses on locomotion learning through vision RGB devices, ambient sensors-based signals, and physiological motions from biosensing devices. First, the data is acquired from five different scenarios-based datasets. Then, we pre-process the data to mitigate the noise from biosensors and extract body landmarks and key points from computer vision-based signals. The data is then segmented using a data windowing technique. Various features are extracted through multiple combinations of feature extraction methodologies, followed by feature reduction using optimization techniques. In contrast to existing systems, we employ both machine learning and deep learning classifiers for locomotion prediction, utilizing a modified body-specific sensor-based Hidden Markov Model and a deep Exponential Residual Neural Network, respectively. System ontology is also presented to elucidate the relationships among the data, concepts, and objects within the system. Experimental results indicate that our proposed biosensor-based system exhibits significant potential for effective locomotion prediction learning.

Status report on novel intraoral scanner-based registration method of axes of rotation of the mandible: Proof of potential applicability and technological glass ceiling of in-house development

A fast, equipment- and radiation-free input methodology for digital articulation is desirable for basic dental and surgical procedures of relatively low complexity. Our team previously proposed an intraoral scanner (IOS)-based strategy for calculating the mandibular axis of rotation using dual bite registrations in open and closed positions. In this study, we aimed to develop a methodology for validating this registration system using a state-of-the-art jaw motion tracking device (Modjaw). The bite alignment accuracy of the scanner used ( Medit i600 ) proved sufficient for the task; however, its technical settings allow processing of only a limited number of bite positions, thereby restricting the number of cases that can be analysed. While in-house, insoluble technical limitations restricted the number of participants to four, significantly limiting the generalizability of the results, several main trends nevertheless appear to emerge: We verified our previous results regarding the error-induced effect ratios (EcD and AEcFE) and provided strong in-vivo support for our earlier in-vitro findings. Notably, alignment of axes obtained with our IOS-based method closely resembled the “ parallel error direction ” described previously. The reproducibility of our methodology approaches the desired 0.1 mm error threshold, but when compared with our control tool (Modjaw), it does not yet meet this criterion. The level of inaccuracy suggests that the goal remains achievable; with protocol modifications, the method could become an acceptable alternative for virtual articulation. Our findings highlight persistent clinical dilemmas and underline that many patient safety concerns remain unresolved, even in the era of advanced digital technologies. We proposed a renewed digital version of the conventional analogue control setup of articulation methods by dynamic motion registers, as a potential strategy to establish and maintain patient safety standards. Although integrating different digital platforms poses significant technological challenges, automated metrics and comparative setups offer advanced possibilities compared to the analogue experimental era.

Sardine: A modular framework for developing data acquisition and near real-time analysis applications

We present Sardine, a software framework built with .NET to control experimental setups through the reliable execution of dynamic networks of independent modules, where each module can interface with a hardware device (e.g., camera, motor) or represent an operation over data (e.g., image filter, data stream). The Sardine framework delivers robust fault-tolerant hardware control and eliminates downstream delays by implementing dedicated data processing queues for each module. Any .NET class can be seamlessly adapted into a Sardine module, enabling effortless integration with existing codebases. Sardine’s modular architecture ensures flexibility to accommodate changes in experimental paradigms, simplifying the adaptation of essential features, such as swapping hardware components or redefining the logic of their interactions. The core of Sardine is a novel aggregation system that connects modules in two layers, designed to streamline complex workflows such as those in microscopy applications. The first layer (link layer) enables concurrent operations between modules, such as synchronizing a camera module with a stage controller or a laser module, while maintaining a dependency tree to ensure devices operate as intended. For example, in an imaging experiment, Sardine oversees individual module malfunctions, such as a camera failure or stage misalignment, gracefully handling errors and dynamically restoring functionality to impacted network segments to minimize disruption. The second layer (data layer) facilitates the transmission of information across modules by associating them with operations that produce, consume, or transform data. In an imaging context, this could involve processing raw image data from a camera module, passing it through a real-time analysis module for feature detection, and forwarding the results to a visualization module for immediate feedback. This two-layer architecture ensures seamless data flow and robust error handling, making Sardine ideal for dynamic and complex experimental setups. Sardine also integrates logging, a metadata collection system, and tools to create graphical applications.

The effects of early life pain and juvenile fear conditioning on CRF-receptor expression in the amygdala and hypothalamus of the juvenile rat

Early life pain and stress have lasting consequences on nervous system development that can interact with later stress or trauma to create a susceptibility to fear, anxiety, depression and chronic pain among other psychological disorders. Recent work has identified changes in corticotropin releasing factor signaling in limbic system structures, such as the amygdala and hypothalamus, as a key mechanism behind these changes – albeit in a sex-dependent manner. CRF has two major receptors, CRFR1 and CRFR2 which have also been shown to play key roles in fear and pain expression. The current work examines the effects of early life pain designed to mimic the neonatal medical trauma that occurs in the Neonatal Intensive Care Unit (NICU), paired with a juvenile trauma in the form of fear conditioning, on expression of crhr1 and crhr2 mRNA in the central nucleus (CeA) and basolateral nucleus (BLA) of the amygdala as well as the paraventricular nucleus (PVN) and ventromedial nucleus (VMH) hypothalamus of the juvenile rat. While prior work has demonstrated that early life pain significantly impacts expression of the CRF ligand mRNA, this study examines the effects of early life pain and stress, as well as adolescent fear conditioning, on CRF receptor expression. The data demonstrate that early life pain and fear conditioning have only modest effects on CRF receptor expression in the amygdala and hypothalamus in a sex dependent manner. In both sexes, fear conditioning increased crhr2 mRNA in the CeA only in neonatally undisturbed subjects. In addition, there was a trend towards altered crhr2 mRNA following neonatal manipulation in the PVN. In females specifically, we observed significant changes in crhr2 mRNA expression following fear conditioning in the right BLA. There were no female-specific changes following neonatal pain and stress. In males, we observed significant changes in crhr1 mRNA in the posterior PVN and trends toward changes in crhr2 mRNA in the CeA and VMH following neonatal manipulation. Together, these data confirm prior work that early life pain and stress alter the neural circuitry of pain and stress in a sex-specific manner. However, given the limited changes observed, in it unlikely that CRH receptor alterations are a major mechanism of action of early life pain.

Management of acute acquired comitant esotropia using prisms and vision therapy

Purpose Acute acquired comitant esotropia (AACE) once considered rare, is now becoming more prevalent. Surgery in these cases can get delayed when the strabismus angle varies or if the patient is unwilling. In such cases optical management and vision therapy can be considered. We present the outcomes of such management for AACE. Methods This retrospective study included a cohort of patients diagnosed with non-accommodative or non-neurological AACE, managed in our institute between January to October, 2023. Prisms were prescribed to patients experiencing constant double vision. All patients were given vision therapy exercises. Eso deviation and divergence range were measured pre and post therapy. Results Fourteen patients were included. Of these, 5 patients (with eso deviation ≤20PD) were relieved with vision therapy exercises alone. For the remaining 9 patients, prisms were prescribed along with vision therapy. Post-vision therapy, 2 patients could achieve binocular single vision without prisms, and in 4 patients the prism power was reduced. In the remaining 3, full prism correction was warranted, one of them opted for surgery. The median reduction in eso deviation by 6.5PD (range:2–25PD) and increase in divergence by 7PD (range:0–17PD) after vision therapy was significant (p < 0.03). Conclusion Non-surgical intervention reduced the eso deviation in 79% (11/14) of patients. Certain types of AACE seem to be amenable to vision therapy and/or optical management. Temporary Fresnel prisms can be considered in majority of these patients and tapered away later, if their divergence range can be improved with vision therapy.