Although many clinical researchers are devoted to discovering the pathogenesis of early fusion of sutures in craniosynostosis, the behavior of the perisynostotic area after surgical excision of the fused suture is poorly understood. In this article, the authors aimed to evaluate the outcomes of extended coronal suturectomy on the shape of the fronto-orbital area. From January 2018 to January 2025, 36 patients with unicoronal synostosis underwent extended suturectomy from the anterior fontanelle to the zygomaticofrontal junction under loupe magnification. After at least 12 months of postoperative follow-up, 83.33% achieved good results (Whitaker classes I-II), 5.55% appeared likely to require future fronto-orbital augmentation (Whitaker class III), and 11.11% required fronto-orbital reconstruction (Whitaker class IV). It appears that with careful follow-up, postoperative management, and early detection, extended suturectomy could be a safe and effective primary treatment option in nonsyndromic unicoronal synostosis, compared to more advanced surgeries.

Hydrocephalus is characterized by excessive cerebrospinal fluid accumulation that enlarges the ventricles and compresses brain tissue, potentially leading to neurological dysfunction. Imaging-based assessment of the ventricular system is vital for clinical decision-making. This study developed an automated deep learning algorithm for hydrocephalus classification from brain CT slices. The dataset comprised CT scans from 55 hydrocephalic patients and 31 healthy controls collected at Loghman-Hakim Hospital, Tehran, Iran. Two strategies were compared: classical machine learning and deep learning (DL). In the first approach, LBP, distance LBP (d-LBP), and angle LBP (alpha-LBP) features were extracted and classified using kNN, random forest, and bagging. In the second, CT images were directly fed into a custom CNN for end-to-end classification without manual segmentation or preprocessing. The CNN achieved 99.53% accuracy, 0.9969 AUC, 99.66% precision, 99.21% recall, and 99.43% F1-score, outperforming all classical models. This AI-powered tool demonstrates high accuracy and real-time capability, showing promise for neurosurgical decision-making in emergency or resource-limited settings where CT is the primary imaging modality. Despite the limited dataset size, the results indicate strong potential, warranting further validation on larger cohorts.

Remote sensing is crucial for monitoring decadal-scale snow cover dynamics in response to climate change across mountainous ecosystems. This study analyzes spatiotemporal snow cover trends and their driving factors using satellite observations, topographic data, and climate variables. MODIS snow product (MOD10A1) data from 2000 to 2022 were used to assess snow cover changes across 69 High Mountain Ecosystems (HME) in the Middle East. We analyzed data from January to April and October to December to capture seasonal snow cover dynamics. A linear regression method detected significant trends in the Normalized Difference Snow Index (NDSI), while a pixel-based Random Forest (RF) regression model assessed environmental drivers influencing NDSI variability. Results show that northern and northwestern humid regions generally experienced increasing NDSI, whereas central and southern arid regions exhibited a decline, highlighting spatial and temporal heterogeneity in snow cover trends. Temperature and precipitation change significantly influenced NDSI patterns, suggesting climate variability plays a critical role in snow cover disturbances. RF analysis identified mean annual temperature, precipitation changes, and mean annual precipitation as the top three drivers of NDSI variability. Future research should focus on the impact of extreme weather events on snow cover. Additionally, refining the methodology with higher-resolution data across diverse climate zones could enhance predictive accuracy.

Craniosynostosis (CSO) is characterized by premature fusion of skull sutures in infants. This early closure of one or more main sutures can lead to various skull and facial deformities and may cause developmental delay in children. Early diagnosis, crucial for effective treatment, traditionally relies on physical examination and 3D cranial imaging, which are often inaccurate or with the risk of X-ray exposure. This study presents a fully-automated deep learning-based method for diagnosing common types of single suture CSO using routine digital photographs of infants' heads. We employed a two-stage approach involving head segmentation and CSO type classification. First, mask region-based convolutional neural network (Mask R-CNN) was used for accurate head segmentation, achieving an average precision of 97.60 ​% and an average recall of 96.20 ​%. The segmented images were then classified into different CSO types using a modified VGG11 neural network. The classifier attained a training accuracy of 99.74 ​% and a test accuracy of 94.44 ​%, with high sensitivity and specificity for uni-coronal, metopic, and sagittal types. Our method illustrates high reliability and accuracy, offering non-invasive, accessible and accurate diagnostic instrument for early detection and patient screening.

Hydrocephalus is excessive accumulation of cerebrospinal fluid within the cerebral ventricles. It has a complex pathogenesis with various causes. Periventricular edema refers to the abnormal accumulation of fluid in the brain tissue surrounding the cerebral ventricles, an indicative of elevated intracranial pressure or disruption in cerebrospinal fluid flow. Periventricular edema can serve as one of the severity indicators of hydrocephalus, and can assist physicians in predicting the outcomes of treatments and determining appropriate therapeutic interventions. In this study, our goal is to identify periventricular edema in hydrocephalus disease. In this regard, the smoothing-sharpening image filter (SSIF) algorithm is applied to enhance hydrocephalic CT images due to the low quality of CT images and the ambiguity between the boundaries of periventricular edema, ventricles, and other brain regions. Some well-known deep learning models including UNet, PSPNet, LinkNet and FPN are suggested to segment periventricular edema. From the obtained results, the FPN model, compared to the other models, achieves the best evaluation criteria with AUC, dice score, F1-score, precision, and recall values of 95 %, 93 %, 91 %, 91 %, and 92 %, respectively.

Achieving sustainable agriculture requires balancing high productivity with minimal environmental impact, a challenge that is ongoing for both farmers and policymakers. This study proposes an innovative approach by integrating life cycle assessment (LCA), data envelopment analysis (DEA), causal loop diagrams (CLD), and the DPSIR (Driving forces, Pressures, State, Impacts, Responses) framework to evaluating regenerative agriculture systems (RAS) in Central Europe. Focusing on wheat and maize production in Slovakia, we assessed scenarios involving different herbicides containing glyphosate and pyridine compounds, as well as the use of cow manure. LCA was first applied to quantify the environmental impacts of each scenario. These results were then used along with the crop yield data in DEA to assess environmental efficiency in the various scenarios, taking into account both environmental and economic goals. CLD and DPSIR frameworks were employed to support a systems-level interpretation of the findings. The LCA results indicated that the complete removal of two key herbicides (Scenario 4) for durum wheat and maize production led to the lowest environmental impact but also the lowest yield. In contrast, applying two key herbicides slightly increased environmental impacts but resulted in higher yields. DEA results indicate that Scenario 1, which incorporates two key herbicides, was the most environmentally efficient for durum wheat, while both Scenarios 1 and 3, which include one key herbicide, were effective for maize. Overall, while the environmental burden differences between scenarios were relatively small, the differences in crop yields were substantial. Therefore, scenarios involving at least one key herbicide proved the most environmentally and economically optimal.

Identification of brain tumors border and determination of their possible pathology in MR images is an important step in pre-operation analyzing of this serious medical condition. Manual segmentation and classification of brain tumors could be challenge full in neurosurgical practice because of vast differences between brain tumors characteristic such as shape, border irregularity, consistency and etc. as well as interobserver variations. To solve this problem, some automatic methods have been proposed for brain tumors segmentation or classification during recent years, but an intelligence-based method for simultaneous identification of tumor type and tumor border in MR images has not proposed till now. Here, we have planned a unique automatic model includes a common encoder for feature representation, one decoder for segmentation and a multi-layer perceptron for classification of three common primary brain tumors (meningiomas, gliomas and pituitary adenomas) in brain MR images. The proposed model was examined on a brain tumor images dataset and the output were evaluated in both multi-task and single-task learning model. The multi-task learning model gains significant improvement in simultaneous classification and segmentation of brain tumors with promising accuracy of 97% for each task. So, this model could serve as a primary screening tool for early diagnosis of common primary brain tumors in general practice with a high success rate.

Wildfires have a significant impact on ecosystems worldwide, especially on the degradation of arid and semi-arid rangelands. This research focuses on assessing the effects of wildfires on the habitat of Trigonella elliptica, a valuable herb species found in the central rangelands of Iran. To achieve this, the Random Forest (RF) algorithm has been deployed to predict T. elliptica habitat and fire hazard using socio-environmental variables in Yazd province, Iran. 225 fire points and 103 habitat locations were used for model training and testing. The IncNodePurity index and Probability Curves (PC) have been utilized to determine the influence of socio-environmental variables. The combination of the prediction maps of the habitat and wildfires pointed out the possible damage due to fire. The high performance of the RF model is confirmed by the area under the curve (AUC) and the true skill statistic (TSS) values (0.90 and 0.81 for the habitat; 0.92 and 0.82 for the wildfire). The importance assessment of variables revealed that elevation, slope, and precipitation are the most influential variables in the distribution of T. elliptica, while distance to roads, population density, and wind speed are the key factors affecting wildfire occurrence. In the final map, a comparison of different regions of T. elliptica habitat under fire hazard with fire-free habitats using Kruskal-Wallis and Dunn tests indicated that the fire hazard in the T. elliptica habitat is a serious concern. Since the areas with the highest fire hazard and the highest presence of T. elliptica cover approximately 2311.38 km2, neglecting these regions could lead to the gradual reduction of T. elliptica, and create conditions for secondary succession dominated by less valuable annual species. The findings of this study underscore the importance of implementing fire management strategies, protection projects, and continuous monitoring to ensure the safety and conservation of the T. elliptica habitat.

The degradation of land (LD) is a major concern for the health and sustainability of natural resources. It is primarily caused by the deterioration of vegetation and soil. In this study, we focus on assessing the risk of LD in the Bakhtegan basin in Iran, one of the arid and semi-arid ecosystems. Our objective is to predict LD hazard and vulnerability maps, and then combine them to identify areas at high risk. To predict LD hazard, the Support Vector Machine (SVM) algorithm was used with 179 LD locations and twelve variables, including land use, lithology, rainfall, temperature, distance to the stream, elevation, aspect, slope, curvature, distance to the road, Normalized Difference Moisture Index (NDMI), and population density. The LD hazard map was evaluated using five error statistics extracted from the contingency table. For LD vulnerability mapping, eight criteria were deployed, including land use, population density, Normalized Difference Vegetation Index (NDVI), livestock density, groundwater quantity and quality, Salinity Index (SI), and migration. These criteria were weighted using the integrating eDPSIR framework and Analytic Network Process (ANP) methods. The results show that low-altitude areas which have low rainfall and high temperatures face the highest LD hazard. Additionally, the western and northwestern regions of the basin are more vulnerable compared to other areas due to factors such as land use and vegetation cover. Lastly, the LD risk map reveals that some 7.56 % of the region falls into the high-risk classification, totaling 2413.37 km2. Notably, salt lands emerge as the most at-risk land use, with 77 % under high risk, with rain-fed agricultural land following as the second-highest risk class. These findings underscore the importance of considering LD risk in land management strategies.

Objective: Anterior plagiocephaly is a condition in which the unicoronal suture is prematurely fused and the skull shape will change due to asymmetric growth. Methods: This is a retrospective study describing the unilateral limited frontal osteotomy for remodeling deformed areas in the frontal and orbital bone and its pros and cons. Results: Twenty-eight patients were included in the study, with a mean age of 16.8 (±11.7) months. Mean intraoperative bleeding was 78.1 (±23.6) mL. One (3.57%) patient developed postoperative bleeding, around 200 mL. After 12 months, all patients (100%) had grade I Whitaker. Conclusion: The described technique is safe and may have promising short-term outcomes for the correction of anterior plagiocephaly.

Objective: craniosynostosis (CSO) is a congenital disorder resulting from early closure of cranial sutures in newborns, while could cause significant cosmetic and neurodevelopmental problems. As a standard method, different craniometric indices are measured directly from child head or from their 3D CT scan of skull for diagnosis or in post-operative follow-up period. We propose a novel telehealth-compatible deep learning neural network-based method for identifying different craniometric indices in non-syndromic CSO patients 2D photo-graphic data.Methods: 624 pre-operative and post-operative top-down cranial digital images of 145 craniosynostotic infants (59 sagittal, 55 metopic and 31 unicoronal synostosis) who had surgery at Mofid Children's Hospital, Tehran, Iran were used in a deep learning neural network algorithm. Head boundary was defined by a faster region-based convolutional neural network (Faster R-CNN) and then different cranial indices (cranial index (CI), cranial vault asymmetry index (CVAI), anterior-posterior width ratio (APWR), anterior-midline width ratio (AMWR) and left-right height ratio (LRHR)) were calculated from segmented images. Accuracy, sensitivity and specificity were calculated for software versus specialist data association between cranial indices were evaluated with inter-class correlation coefficients.Results: The head border was segmented in the proposed images with accuracy of 88.67 +/- 1.94 in comparison with standard hand made procedure with a sensitivity of 86.91 +/- 3.75 and specificity of 88.60 +/- 4.81. Among calculated cranial indices, significant decrease in CI value is most useful for diagnosis of sagittal synostosis (CIsagittal= 71.97 +/- 4.33), significant increase in CVAI value and significant decrease in LRHR value is most appropriate for unicoronal suture synostosis diagnosis (CVAIunicoronal= 6.79 +/- 3.80 and LRHRunicoronal = 0.91 +/- 0.05) and significant decrease in APWR and AMWR values could be indicator of metopic synostosis (AMWRmetopic = 0.77 +/- 0.04 and APWRmatopic = 0.83 +/- 0.05). Conclusion: Deep learning neural network algorithms could have high levels of capability in calculating cranial indices from routine 2D digital images of non-syndromic craniosynostotic children and act as a substitute for optical scanner or 3D CT-based craniometrics. This method could act as a corner stone for developing a software for a mobile platform that that would allow for screening by tele-medicine or in a primary care setting.

In recent years, drought has become a global problem in arid and semiarid regions, especially through detrimental effects on the agricultural, ecological, and socioeconomic sectors. Accordingly, the purpose of this study is to investigate the performance of the Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Soil Water Index (SWI) in identifying and analyzing agricultural drought in the Karkheh River Basin. In order to calculate these indices, MODIS productions were used in the three months of March, April, and May during 2001–2018. First, drought periods were extracted based on the VCI, TCI, and SWI. Then, the performance of each of the indices was analyzed using the Standardized Precipitation Index (SPI) in 1-, 3- and 6-months scales and Pearson correlation test. The results of VCI analysis showed that the most severe and widespread drought occurred from March to May in 2008. Regarding the results of the TCI, the value of the index in most years showed no drought or mild drought conditions. Also, based on the results obtained from the SWI, the amount of drought in the southern parts of the basin during the months of March to May is higher in all years. According to the results of the VCI, the maximum correlation coefficient was observed with SPI3 in March and April, and with SPI6 in May equal to 0.51, 0.55, and 0.49, respectively. Also, there was a weak correlation between the VCI and SPI1. Investigation of the correlation between TCI and SPI in different timescales showed that the TCI has less correlation than the VCI with SPI1, SPI3, and SPI6. The highest correlation for the TCI was observed with SPI3 equal to 0.27. Finally, the SWI showed a higher correlation with SPI1 equal to 0.51, 0.54, and 0.41 for March, April, and May, respectively, and also had less correlation with the SPI3 and SPI6 indices. Comparison of drought indices in the Karkheh basin demonstrates that the VCI is the best indicator to monitor agricultural drought conditions because of the stronger correlation with SPI3 and SPI6, but none of the indices is considered superior to the rest in all situations.

Every year, hydrocephalus affects many people, whether they are infants or adults with higher incidence in pediatric group. This means that a large number of families affected by this disease, and automatic determination of hydrocephalus in medical images could help people who are affected with this disease and their condition is at a dangerous stage. In this research, we try to implement an algorithm that could detect hydrocephalus from CT images. Our work is divided into two stages of implementing the proposed algorithm and comparing it with the results of similar researches. We try to design and implement all steps from scratch. The proposed model reaches the result with appropriate accuracy but slower running time. Our Method is able to detect hydrocephaly from 40 patients CT Images and calculate ventricular indices with accuracy of 64% to 93% in comparison with manually extracted ventricular indices by a neurosurgeon. In addition, we also checked the K-nearest neighbor (KNN) and Watershed methods and concluded that Region Growing is the best among this category.

Background: One controversial question in Carpal Tunnel Syndrome (CTS) diagnosis is whether magnetic resonance imaging (MRI) and Ultrasound (US) imaging tools have any relationship with electrodiagnostic (EDX) study. The objective of this study is to determine the possible correlation between MRI and US measurements with EDX parameters. Methods: Both US and MRI of the median nerve were simultaneously performed in 12 confirmed CTS wrists, at two levels of forearm distal fold (proximal) and the hook of the hamate (distal), to measure various anatomic parameters of the nerve. EDX parameters of median motor distal latency (DL) and median sensory proximal latency (PL) were evaluated in milliseconds. Results: Nerve cross-sectional area (CSA), measured by MRI, correlated with sensory PL at distal level (p = 0.015). At proximal level MRI, nerve width and width to height ratio also correlated with motor DL (p = 0.033 and 0.021, respectively). Median nerve CSA proximal to distal ratio correlated with sensory PL (p = 0.028) at MRI. No correlation was found between US and EDX measurements. Conclusions: Median nerve MRI measurement of nerve CSA at hook of the hamate (distal) level or CSA proximal to distal ratio correlated with EDX parameter of sensory PL. On the other hand, nerve MRI width and width to height ratio at distal level correlated with motor DL in EDX. Level of Evidence: Level III (Diagnostic).

The quantitative understanding of vegetation vulnerability as a major example of terrestrial ecosystems under hydrometeorological stress is essential for environmental risk preparedness and mitigation strategies. The aim of this study was to develop a new quantitative vegetation vulnerability map using benchmark and standalone machine learning (ML) algorithms (e.g., RF, SVM and Maxent), as well as influencing variables (evaporation, rainfall, maximum temperature, slope degree, elevation, topographic wetness index, distance from river, aspect, land use), in the South Baluchistan basin, Iran. An ensemble model was developed based on selected standalone ML algorithms. A vegetation vulnerability index (VVI), based on remote sensing indices (NDVI, VCI, LST, and TCI), was used to monitor vegetation conditions and changes. Five evaluation metrics for the confusion matrix (accuracy, precision, bias, Probability of Detection (POD), False Alarm Ratio (FAR)) and ROC-AUC were used to measure the predictive performance of the ensemble model and VVI. The optimum values for accuracy, precision, bias, POD, FAR, and ROC-AUC were obtained as 0.89, 0.88, 1.02, 0.91, 0.11, and 0.946, respectively for the ensemble model. Based on remote sensing data, VVI achieved a 0.923 prediction rate in vegetation vulnerability mapping (the efficiency of the ensembled model was somewhat better than VVI). Based on the results obtained from the ensemble model, precipitation (PRD = 20.61), maximum temperature (PRD = 12.31), evaporation (PRD = 5.53), and distance from the river (PRD = 2.62) were found to be the most important variables. The methodology as presented in this study provides valuable information in a large area and can be easily modified for other case studies by adding different influencing variables.

In this randomized clinical trial, we look for the following questions' answer: How does the integration of LMC affect (1) upper extremity (UE) function, (2) grip strength, and (3) lateral and palmar pinch strength in children with cerebral palsy (CP), in comparison with conventional rehabilitation methods? Twenty patients were randomly assigned to LMC (case) or conventional (control) groups. The grip, lateral and palmar pinch forces increased in case group patients more than control group both at the end of intervention (P < .001 for all three), and at 20 weeks' follow-up (P values 0.035, 0.002, and 0.002). The Quality of Upper Extremity Skills Test (QUEST) score changes were similar between two groups, except for grasp score at the end of step 2 and 3 (P = .04 and 0.01, respectively). The addition of LMC to the rehabilitation program of patients with CP may improve the UE motor function outcomes.

Objective: To investigate the effectiveness of minimally invasive suturectomy (MIS) with cranial remolding orthosis (CRO) for treatment of patients with single-suture craniosynostosis. Methods: In this multicenter prospective study, all patients included underwent MIS followed by postoperative CRO. The values of the cephalic index (CI) and cranial vault asymmetry index (CVAI) were measured and recorded before the surgery, at initiation of CRO, and at the cessation of CRO. The total cranial volume (TV), anterior hemisphere cranial volume (AV), and AV to TV were also acquired with a non-contact optical scanner. All patients were followed up at least until the end of the twelfth month of age. Results: A total of 38 patients were included. The average operative time including anesthesia was 95.38 minutes. The average length of hospital admission was 2.5 days. There was a statistically significant difference in CI values for the patients with sagittal craniosynostosis (p < 0.05). The improvement in CVAI values were statistically significant in all three groups of participants (p < 0.001). The AV and TV from initiation to cessation of CRO treatment were statistically significant in all three groups of participants (p < 0.001). Regarding the AV to TV ratio, there was a significant difference from initiation to the cessation of CRO treatment in sagittal and metopic groups of participants (p < 0.05). The average daily hours of CRO wearing was 18.54 hours. Conclusions: The findings of this investigation complement those of earlier studies and showed that MIS with CRO treatment is a good strategy with satisfactory results for patients with single-suture craniosynostosis.

Background: Craniosynostosis, a malformation caused by premature closure of one or more cranial sutures, is a rare congenital disability usually of unknown cause; however, it is often associated with assisted reproductive technology. Given the increasing prevalence of craniosynostosis and the use of the in vitro fertilization (IVF) method, the authors evaluated the association between IVF and the prevalence of craniosynostosis. Methods: This retrospective study reviewed records of patients with nonsyndromic craniosynostosis who underwent surgery in Mofid Hospital, a tertiary children's hospital affiliated to Shahid Beheshti University of Medical Sciences, between 2010 and 2019. Results: A total of 200 patients aged one month to 7 years old, were evaluated. Out of 200 patients, 43% were plagiocephalic, 39% trigonocephalic, 8.5% scaphocephalic, 8% brachiocephalic, and 1.5% were mixed. Nine (4.5%) patients had received clomiphene citrate. Eight (4%) mothers had become pregnant under IVF, and they all had used clomiphene citrate for ovulation stimulation. No use of artificial insemination was reported. Of the eight patients whose mother had become pregnant through IVF, three were trigonocephalic, and five were plagiocephalic. Conclusions: Without a control group, we are not able report the statistical results confirming or denying a link between craniosynostosis and infertility treatment. However, 4% prevalence of IVF use among craniosynostosis patients is significant. Further studies with a broader statistical community are suggested in this regard.