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Despite the widespread applications of C–H functionalization, controlling site selectivity remains a significant challenge. Covalently attached directing groups (DGs) served as ancillary ligands to ensure ortho -, meta - and para -C–H functionalization over the last two decades. These covalently linked DGs necessitate two extra steps for a single C–H functionalization: introduction of DG prior to C–H activation and removal of DG post-functionalization. Here we report a temporary directing group (TDG) for meta -C–H functionalization via reversible imine formation. By overruling facile ortho -C–H bond activation by imine- N atom, a suitably designed pyrimidine-based TDG successfully delivered selective meta -C–C bond formation. Application of this temporary directing group strategy for streamlining the synthesis of complex organic molecules without any necessary pre-functionalization at the meta position has been explored. Site-selective C–H functionalization still faces some challenges, such as the introduction and removal of an appropriate directing group. Here, the authors introduce a temporary directing group for selective meta -C–H functionalization of 2-arylbenzaldehydes via reversible imine formation.

Background Artificial intelligence (AI) techniques are increasingly applied to magnetic resonance imaging (MRI) for detecting temporomandibular joint (TMJ) anomalies; however, their overall diagnostic accuracy and generalizability remain uncertain. Objectives To systematically review and meta-analyse the diagnostic performance of AI models for TMJ anomaly detection on MRI and to identify factors influencing model performance. Methods A comprehensive search of PubMed, Scopus, Embase, and Web of Science was conducted for studies published between January 2015 and September 2025. Two reviewers independently screened and extracted data. Eligible studies developed and tested AI, machine learning, or deep learning models on human TMJ MRI and reported quantitative performance metrics. Risk of bias was assessed using the QUADAS-2 tool. Pooled sensitivity and specificity were estimated using a bivariate random-effects model, while pooled accuracy was derived using logit transformation. Heterogeneity ( I 2 ) was explored through subgroup analyses by model architecture and validation strategy. Results Fourteen studies were included in the systematic review, of which six met the criteria for meta-analysis. Across these six studies, 18 models were analyzed for accuracy, 29 for sensitivity, and 24 for specificity. The pooled diagnostic accuracy was 0.487 (95% CI 0.403–0.571), with pooled sensitivity and specificity of 0.399 (95% CI 0.348–0.450) and 0.399 (95% CI 0.343–0.456), respectively, all showing substantial heterogeneity ( I 2  > 90%). Subgroup analyses indicated that advanced architectures such as ResNet-18, Inception v3, and EfficientNet-b4 achieved higher and more consistent diagnostic performance. Conclusions Advanced deep learning architectures such as ResNet-18, Inception v3, and EfficientNet-b4 demonstrated superior diagnostic performance for detecting temporomandibular joint anomalies on MRI. These findings highlight the potential of AI-assisted MRI interpretation to improve diagnostic consistency, efficiency, and early detection of TMJ pathology. However, substantial heterogeneity and limited external validation currently limit clinical translation. Standardized multicenter studies and transparent model validation are essential to ensure reliable integration of AI tools into clinical TMJ imaging workflows.

Background Cerebral computed tomography angiography (CTA) is widely used to assess neurovascular disorders, but venous contamination often obscures arteries. Optimizing bolus-tracking thresholds is crucial, yet patient factors influencing contrast dynamics and the value of radiation dose indices in head CTA remain unclear. Objectives To optimize bolus-tracking thresholds in cerebral CTA by examining patient-related influences on enhancement and radiation metrics. Methods 126 adults undergoing cerebral CTA were evaluated in this prospective study. Demographics, physiologic parameters, peak enhancement time (PET), peak enhancement attenuation (PEA), and dose indices (CTDIvol, SSDE) were recorded. Linear regression identified predictors of enhancement. Two blinded radiologists graded venous contamination. ROC analysis, including age subgroups, determined the optimal HU threshold. Results Median age was 55.5 years; 70% were male. PET rose with age (+0.086 s/year, p < 0.001) and was shorter in females (–2.39 s, p = 0.003). PEA increased with threshold (+1.03 HU/unit, p < 0.001). Arterial enhancement was higher in females (+40.7 HU, p < 0.001) and patients ≥60 years (+70 HU, p < 0.001). Venous enhancement correlated with PET (p = 0.023) and systolic pressure (p = 0.002). ROC analysis showed an optimal threshold of 105 ± 5 HU (AUC = 0.634; sensitivity 88.4%, specificity 77.1%). CTDIvol, but not SSDE, correlated with weight (p = 0.015). Conclusion Intrinsic (age, gender) and extrinsic (threshold) factors shape CTA enhancement. A 105 ± 5 HU threshold reduces venous contamination, especially in younger patients. CTDIvol remains the preferred dose index. Findings support individualized, resource-efficient CTA protocols aligned with UN SDGs 3, 9, and 12.

Objectives Degenerative lumbar spine disease, a leading cause of chronic pain and disability in older adults, results from the progressive degeneration of intervertebral discs. This systematic review evaluates the role of mean kurtosis (MK) as a diffusion kurtosis imaging (DKI) parameter in the early diagnosis of degenerative spine disease and its potential to enhance patient outcomes. Methods A systematic review was conducted following PRISMA guidelines, with a comprehensive search yielding 7,290 articles. After screening, three studies met the inclusion criteria. Quality assessment was performed using the QUADAS tool, considering studies with a score of ≥10 as high-quality. Data extraction focused on DKI parameters, particularly MK, in assessing early disc degeneration. The study was registered in PROSPERO (CRD42024554902) on June 5, 2024. Results Findings indicate that MK plays a crucial role in detecting microstructural changes in the intervertebral disc space of the lumbar spine. These changes closely correlate with clinical symptoms and the extent of degeneration observed on conventional MRI. DKI-derived MK appears to offer greater sensitivity in identifying early-stage microstructural degeneration compared to traditional imaging methods Conclusions MR DKI demonstrates significant potential for detecting subtle, early changes in lumbar spine degeneration. Integrating DKI into clinical practice could enhance diagnostic accuracy, enable earlier interventions, and ultimately improve patient outcomes.

Objectives Degenerative lumbar spine disease, a leading cause of chronic pain and disability in older adults, results from the progressive degeneration of intervertebral discs. This systematic review evaluates the role of mean kurtosis (MK) as a diffusion kurtosis imaging (DKI) parameter in the early diagnosis of degenerative spine disease and its potential to enhance patient outcomes. Methods A systematic review was conducted following PRISMA guidelines, with a comprehensive search yielding 7,290 articles. After screening, three studies met the inclusion criteria. Quality assessment was performed using the QUADAS tool, considering studies with a score of ≥10 as high-quality. Data extraction focused on DKI parameters, particularly MK, in assessing early disc degeneration. The study was registered in PROSPERO (CRD42024554902) on June 5, 2024. Results Findings indicate that MK plays a crucial role in detecting microstructural changes in the intervertebral disc space of the lumbar spine. These changes closely correlate with clinical symptoms and the extent of degeneration observed on conventional MRI. DKI-derived MK appears to offer greater sensitivity in identifying early-stage microstructural degeneration compared to traditional imaging methods Conclusions MR DKI demonstrates significant potential for detecting subtle, early changes in lumbar spine degeneration. Integrating DKI into clinical practice could enhance diagnostic accuracy, enable earlier interventions, and ultimately improve patient outcomes.

Background Breast cancer remains a significant global health concern, with early diagnosis and risk factor identification crucial for improving outcomes. Diffusion-Weighted Imaging (DWI) and Apparent Diffusion Coefficient (ADC) measurements have emerged as promising tools in breast cancer diagnostics. However, the influence of hormonal status on these measurements remains unclear. Objective This scoping review aims to synthesize current evidence on how hormonal changes in pre- and post-menopausal women influence ADC values of benign, malignant, and fibroglandular breast tissues. Method Following the Arksey and O'Malley framework, we conducted a comprehensive search of Scopus, Embase, and PubMed databases for relevant studies published between January 2000 and 2021. Inclusion criteria encompassed 1.5 Tesla MRI studies reporting ADC values in female subjects, considering menopausal status. Results Six studies meeting the inclusion criteria, involving 612 patients, were analyzed. Findings suggest that menopausal status may influence ADC values, with postmenopausal women generally showing lower ADC values in both normal fibroglandular tissue and breast lesions. The impact of menstrual cycle phases on ADC values was less consistent across studies. Conclusions This review highlights the potential influence of hormonal status on ADC values in breast tissues. While DWI with ADC mapping shows promise as a reliable diagnostic tool across different hormonal states, further research is needed to fully understand and account for hormonal influences on ADC measurements. Future studies should focus on longitudinal designs, standardization of DWI protocols, and integration of hormonal status information into breast cancer risk assessment models.

Background Breast cancer remains a significant global health concern, with early diagnosis and risk factor identification crucial for improving outcomes. Diffusion-Weighted Imaging (DWI) and Apparent Diffusion Coefficient (ADC) measurements have emerged as promising tools in breast cancer diagnostics. However, the influence of hormonal status on these measurements remains unclear. Objective This scoping review aims to synthesize current evidence on how hormonal changes in pre- and post-menopausal women influence ADC values of benign, malignant, and fibroglandular breast tissues. Method Following the Arksey and O'Malley framework, we conducted a comprehensive search of Scopus, Embase, and PubMed databases for relevant studies published between January 2000 and 2021. Inclusion criteria encompassed 1.5 Tesla MRI studies reporting ADC values in female subjects, considering menopausal status. Results Six studies meeting the inclusion criteria, involving 612 patients, were analyzed. Findings suggest that menopausal status may influence ADC values, with postmenopausal women generally showing lower ADC values in both normal fibroglandular tissue and breast lesions. The impact of menstrual cycle phases on ADC values was less consistent across studies. Conclusions This review highlights the potential influence of hormonal status on ADC values in breast tissues. While DWI with ADC mapping shows promise as a reliable diagnostic tool across different hormonal states, further research is needed to fully understand and account for hormonal influences on ADC measurements. Future studies should focus on longitudinal designs, standardization of DWI protocols, and integration of hormonal status information into breast cancer risk assessment models.

Purpose Computed tomography angiography (CTA) is a non-invasive procedure to evaluate vascular anomalies. However, the high radiation dose related to the examination is a concern due to its associated cancer risk. Therefore, this study aims to investigate the effect of low tube voltage CT pulmonary angiography (CTPA) on radiation dose and image quality based on body mass index. Methods Eighty patients were included in this study, where an initial 20 patients were scanned with standard dose protocol, irrespective of BMI. The remaining 60 patients were equally allocated to BMI-based protocol, where high and obese BMI was allocated to 120 kVp protocol, normal BMI to 100 kVp protocol, and low BMI to 80 kVp protocol. Dose data and image quality data were collected after the scan. Results The result showed a 60% reduction in radiation dose, a 61% increase in vessel attenuation for the 80 kVp protocol, and an improved qualitative image score. Conclusion In conclusion, tailoring the tube voltage with BMI can be used for CTPA while achieving significant dose reduction without compromising image quality.

Physical inactivity is a key modifiable risk factor for dementia, and physical activity is proposed to confer neuroprotective effects on brain structure and cognitive function. To synthesize longitudinal observational evidence on associations between long-term habitual physical activity and brain structural outcomes and cognitive function across adult age groups. Five databases were searched from inception to August 2024. Eligible studies enrolled adults aged ≥ 18 years without cognitive impairment or dementia, used validated accelerometry or self-report physical activity assessment, and employed longitudinal observational designs with a minimum 12-month exposure-to-outcome window. Risk of bias was assessed using the ROBINS-E tool. Twenty studies were included in the review. Higher habitual physical activity was consistently associated with preserved hippocampal and prefrontal grey matter volume and attenuated white matter microstructural decline. Executive function and processing speed showed the most consistent cognitive associations, with evidence of a dose-response accumulation effect across adulthood. Associations appeared amplified in APOE-ε4 carriers. Bidirectional analyses indicated preserved brain structure also predicted subsequent physical activity, suggesting reverse causation cannot be excluded. All 20 studies were rated \"some concerns\" on the ROBINS-E scale. Longitudinal observational evidence is consistent with the hypothesis that sustained habitual physical activity is associated with preserved brain structure and attenuated cognitive decline, though causal inference is not warranted given the limitations of the observational design.

Purpose Medical technology has gone a long way in diagnosis and characterization of breast tumors. Diffusion-weighted MR imaging is the state of the art for breast screening and diagnosing. The aim of this meta-analysis is to evaluate the diagnostic performances of diffusion-weighted MR imaging in characterization of breast lesions with different b value in 1.5 T MRI. Method An extensive search on Scopus, Embase, and PubMed databases were performed on studies published between January 2000 and 2020. The systematic seek initially yielded 2467 studies, out of which 27 research were covered on this meta-evaluation. The included studies for meta-analysis utilized different b value and noted that the ADC value was highly influenced by the b value, for differential diagnosis of breast tumors. Results The current meta-analysis has shown the ADC values was lower for malignant breast lesions as compared with benign lesions. The recommended mean threshold ADC was 1.25 ± 0.17 × 10 –3 mm 2 /s range from 0.93 to 1.60 × 10 –3 mm 2 /s for differential diagnosis of breast tumors. Sub-group analysis on the bases of b value showed statistically significant differences in the ADC value of benign and malignant breast tumors. Conclusion In conclusion, we noted that b value has a significant effect in calculating the ADC value of the breast lesions as well as ADC threshold value but lacks standardization. The ADC value measurement has a potential for differentiation between benign and malignant breast lesions.