Intrahepatic cholangiocarcinoma (ICC) is a highly malignant primary tumor originating from the intrahepatic bile duct epithelium. Current clinical consensus emphasizes that early detection,timely diagnosis,and R0 resection (when feasible) are critical strategies for improving outcomes. Advances in surgical techniques and multidisciplinary approaches have facilitated precision medicine-guided systemic therapies,including adjuvant and neoadjuvant regimens. These interventions significantly delay postoperative recurrence and improve 5-year survival rates. Systemic treatments have also modestly enhanced prognoses for advanced ICC patients. Artificial intelligence-driven radiomics and deep learning technologies show substantial efficacy in ICC diagnosis,prognostic assessment,and lymph node metastasis prediction,advancing precision medicine applications. Despite these therapeutic advances,survival outcomes-particularly for advanced-stage ICC-remain suboptimal. Future efforts should prioritize molecular signature-guided therapies,novel combination regimens,and artificial intelligence-assisted comprehensive management to establish a molecularly driven precision treatment framework.

Intrahepatic cholangiocarcinoma (ICC) is a highly malignant primary tumor originating from the intrahepatic bile duct epithelium. Current clinical consensus emphasizes that early detection,timely diagnosis,and R0 resection (when feasible) are critical strategies for improving outcomes. Advances in surgical techniques and multidisciplinary approaches have facilitated precision medicine-guided systemic therapies,including adjuvant and neoadjuvant regimens. These interventions significantly delay postoperative recurrence and improve 5-year survival rates. Systemic treatments have also modestly enhanced prognoses for advanced ICC patients. Artificial intelligence-driven radiomics and deep learning technologies show substantial efficacy in ICC diagnosis,prognostic assessment,and lymph node metastasis prediction,advancing precision medicine applications. Despite these therapeutic advances,survival outcomes-particularly for advanced-stage ICC-remain suboptimal. Future efforts should prioritize molecular signature-guided therapies,novel combination regimens,and artificial intelligence-assisted comprehensive management to establish a molecularly driven precision treatment framework.

Flap transplantation is a critical surgical strategy for the reconstruction of tissue defects caused by trauma, tumor resection, and congenital malformations, and its survival rate directly determines surgical efficacy and patient prognosis. Following transplantation, flaps inevitably undergo ischemia-reperfusion (I/R) injury, during which oxidative stress, inflammatory responses, and metabolic disturbances are intricately intertwined, ultimately leading to cellular injury and tissue necrosis. Recent studies have demonstrated that multiple forms of programmed cell death—including apoptosis, pyroptosis, ferroptosis, necroptosis, and PANoptosis—play central roles in flap I/R injury. The extensive crosstalk and molecular interactions among these pathways form a highly complex cell death network. Specifically, apoptosis is mediated by the imbalance of Bcl-2 family proteins and the activation of cysteine-dependent aspartate-specific protease (caspase) cascades; pyroptosis is driven by the NLRP3-caspase-1-GSDMD axis, resulting in membrane pore formation and the release of pro-inflammatory cytokines; ferroptosis is characterized by iron-dependent lipid peroxidation and dysfunction of glutathione peroxidase 4 (GPX4); necroptosis is triggered by the receptor-interacting serine/threonine-protein kinase 1 (RIPK1)-RIPK3-MLKL signaling complex, leading to membrane rupture; and PANoptosis represents an integrated form of inflammatory cell death that coordinates multiple death pathways. Importantly, these forms of programmed cell death are not independent but are interconnected through extensive signaling crosstalk. Key regulatory molecules, including caspase-8, reactive oxygen species (ROS), nuclear factor-κB (NF-κB), and nuclear factor erythroid 2-related factor 2 (Nrf2), collectively modulate the dynamic balance among these pathways. Therefore, the multidimensional interplay and spatiotemporal dynamics of programmed cell death constitute a fundamental pathological basis of flap I/R injury. This review systematically summarizes the latest advances in the mechanisms and interactions of various programmed cell death pathways in flap I/R injury, aiming to elucidate the underlying regulatory network. These insights may provide novel theoretical foundations for optimizing flap protection strategies, improving flap survival, and promoting tissue repair.

Background/Aims: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related mortality worldwide and is driven by metabolic reprogramming that supports tumor growth and progression. A common missense genetic variant (rs2642438, p.A165T) in mitochondrial amidoxime reducing component 1 (MTARC1), identified as protective against liver disease, has been recently associated with lower prevalence of steatosis, cirrhosis, and HCC. However, the mechanistic role of MTARC1 in HCC is unclear. Therefore, we sought to decipher the role of MTARC1 in HCC. Methods: We investigated the role of MTARC1 in HCC by performing siRNA-mediated knockdown across human immortalized HCC cell lines (Hep3B2, HuH7, HepG2 and HepaRG) homozygous for the risk allele (p.A165) and by generating stable CRISPR-Cas9 knockout (KO) models. Next, we assessed the effect of MTARC1 loss on cell proliferation, migration, lipid metabolism, and fatty acid oxidation in vitro, as well as tumor aggressiveness in a subcutaneous xenograft mouse model. Additionally, we performed global proteomics in both in vitro and xenograft models. Results: Transient knockdown of MTARC1 p.A165 reduced proliferation in HCC cell lines. CRISPR-Cas9-mediated stable MTARC1 p.A165 KO in Hep3B2 cells led to decreased neutral lipid intracellular accumulation, enhanced β-oxidation and reduced cell migration. An MTARC1 KO xenograft model had reduced tumor volume. Proteomic analyses of both in vitro HCC cells and xenograft tumors revealed inhibition of oncogenic pathways and activation of anti-proliferative proteins. Conclusions Downregulation of MTARC1 p.A165 inhibits lipid accumulation, dampens tumor-promoting pathways and restricts tumor growth, highlighting MTARC1 as a promising therapeutic target for HCC.

Objective: The variety and efficacy of biomarkers available that may be used objectively to diagnose major depressive disorder (MDD) in adults are unclear. This systematic review aims to identify and evaluate the variety of objective markers used to diagnose MDD in adults. Methods: The search strategy was applied via PubMed and PsycINFO over the past 10 years (2013–2023) to capture the latest available evidence supporting the use of biomarkers to diagnose MDD. Data was reported through narrative synthesis. Results: Forty-two studies were included in the review. Findings were synthesised based on the following measures: blood, neuroimagingeurophysiology, urine, dermatological, auditory, vocal, cerebrospinal fluid and combinatory—and evaluated based on its sensitivity/specificity and area under the curve values. The best predictors of blood (MYT1 gene), neuroimagingeurophysiological (5-HT1A auto-receptor binding in the dorsal and median raphe), urinary (combined albumin, AMBP, HSPB, APOA1), cerebrospinal fluid-based (neuron specific enolase, microRNA) biomarkers were found to be closely linked to the pathophysiology of MDD. Conclusion A large variety of biomarkers were available to diagnose MDD, with the best performing biomarkers intrinsically related to the pathophysiology of MDD. Potential for future research lies in investigating the joint sensitivity of the best performing biomarkers identified via machine learning methods and establishing the causal effect between these biomarkers and MDD.

Objective: The variety and efficacy of biomarkers available that may be used objectively to diagnose major depressive disorder (MDD) in adults are unclear. This systematic review aims to identify and evaluate the variety of objective markers used to diagnose MDD in adults. Methods: The search strategy was applied via PubMed and PsycINFO over the past 10 years (2013–2023) to capture the latest available evidence supporting the use of biomarkers to diagnose MDD. Data was reported through narrative synthesis. Results: Forty-two studies were included in the review. Findings were synthesised based on the following measures: blood, neuroimagingeurophysiology, urine, dermatological, auditory, vocal, cerebrospinal fluid and combinatory—and evaluated based on its sensitivity/specificity and area under the curve values. The best predictors of blood (MYT1 gene), neuroimagingeurophysiological (5-HT1A auto-receptor binding in the dorsal and median raphe), urinary (combined albumin, AMBP, HSPB, APOA1), cerebrospinal fluid-based (neuron specific enolase, microRNA) biomarkers were found to be closely linked to the pathophysiology of MDD. Conclusion A large variety of biomarkers were available to diagnose MDD, with the best performing biomarkers intrinsically related to the pathophysiology of MDD. Potential for future research lies in investigating the joint sensitivity of the best performing biomarkers identified via machine learning methods and establishing the causal effect between these biomarkers and MDD.

Objective: The variety and efficacy of biomarkers available that may be used objectively to diagnose major depressive disorder (MDD) in adults are unclear. This systematic review aims to identify and evaluate the variety of objective markers used to diagnose MDD in adults. Methods: The search strategy was applied via PubMed and PsycINFO over the past 10 years (2013–2023) to capture the latest available evidence supporting the use of biomarkers to diagnose MDD. Data was reported through narrative synthesis. Results: Forty-two studies were included in the review. Findings were synthesised based on the following measures: blood, neuroimagingeurophysiology, urine, dermatological, auditory, vocal, cerebrospinal fluid and combinatory—and evaluated based on its sensitivity/specificity and area under the curve values. The best predictors of blood (MYT1 gene), neuroimagingeurophysiological (5-HT1A auto-receptor binding in the dorsal and median raphe), urinary (combined albumin, AMBP, HSPB, APOA1), cerebrospinal fluid-based (neuron specific enolase, microRNA) biomarkers were found to be closely linked to the pathophysiology of MDD. Conclusion A large variety of biomarkers were available to diagnose MDD, with the best performing biomarkers intrinsically related to the pathophysiology of MDD. Potential for future research lies in investigating the joint sensitivity of the best performing biomarkers identified via machine learning methods and establishing the causal effect between these biomarkers and MDD.

Objective: The variety and efficacy of biomarkers available that may be used objectively to diagnose major depressive disorder (MDD) in adults are unclear. This systematic review aims to identify and evaluate the variety of objective markers used to diagnose MDD in adults. Methods: The search strategy was applied via PubMed and PsycINFO over the past 10 years (2013–2023) to capture the latest available evidence supporting the use of biomarkers to diagnose MDD. Data was reported through narrative synthesis. Results: Forty-two studies were included in the review. Findings were synthesised based on the following measures: blood, neuroimagingeurophysiology, urine, dermatological, auditory, vocal, cerebrospinal fluid and combinatory—and evaluated based on its sensitivity/specificity and area under the curve values. The best predictors of blood (MYT1 gene), neuroimagingeurophysiological (5-HT1A auto-receptor binding in the dorsal and median raphe), urinary (combined albumin, AMBP, HSPB, APOA1), cerebrospinal fluid-based (neuron specific enolase, microRNA) biomarkers were found to be closely linked to the pathophysiology of MDD. Conclusion A large variety of biomarkers were available to diagnose MDD, with the best performing biomarkers intrinsically related to the pathophysiology of MDD. Potential for future research lies in investigating the joint sensitivity of the best performing biomarkers identified via machine learning methods and establishing the causal effect between these biomarkers and MDD.

Xerostomia (dry mouth) is frequently experienced by patients treated with radiotherapy for head and neck cancers or with Sjögren's syndrome, with no permanent cure existing for this debilitating condition. To this end, in vitro platforms are needed to test therapies directed at salivary (fluid-secreting) cells. However, since these are highly differentiated secretory cells, the maintenance of their differentiated state while expanding in numbers is challenging. In this study, the efficiency of three reversible thermo-ionically crosslinked gels: (1) alginate-gelatin (AG), (2) collagen-containing AG (AGC), and (3) hyaluronic acid-containing AG (AGHA), to recapitulate a native-like environment for human salivary gland (SG) cell expansion and 3D spheroid formation was compared. Although all gels were of mechanical properties comparable to human SG tissue (~11 kPa) and promoted the formation of 3D spheroids, AGHA gels produced larger (>100 cells/spheroid), viable (>93%), proliferative, and well-organized 3D SG spheroids while spatially and temporally maintaining the high expression of key SG proteins (aquaporin-5, NKCC1, ZO-1, α-amylase) for 14 days in culture. Moreover, the spheroids responded to agonist-induced stimulation by increasing α-amylase secretory granules. Here, we propose alternative low-cost, reproducible, and reversible AG-based 3D hydrogels that allow the facile and rapid retrieval of intact, highly viable 3D-SG spheroids.
Humans ; Hydrogels/chemistry* ; Acinar Cells/cytology* ; Spheroids, Cellular/cytology* ; Salivary Glands/cytology* ; Gelatin/chemistry* ; Collagen/chemistry* ; Alginates/chemistry* ; Cell Culture Techniques/methods* ; Hyaluronic Acid/chemistry* ; Cell Proliferation ; Cell Survival ; Cells, Cultured

Background: A 2018 study on the global burden of disease, accidents, and risk factors reported that 1.6 million peo ple died in 2017 due to household air pollution. Poor indoor air quality has been highlighted as a contributing factor to respiratory diseases, cardiovascular conditions, and exacerbation of asthma and allergies. A 2019 study estimated that long-term exposure to fine particulate matter (PM2.5) with a diameter of 2.5 micrometers or less reduces average life expectancy by 1.8 years, with more severe effects in highly polluted regions. Additionally, a study by Miller et al. (2007) found that prolonged exposure to PM2.5 increases the risk of cardiovascular diseases, particularly among women. Direct measurement devices are highly effective in determining indoor PM2.5 concentrations, identifying sources of pollution, tracking pollutant dispersion, and monitoring temporal variations. Studies suggest that direct measurement is an accurate, cost-effective method that provides detailed data suitable for local conditions. Aim: To investigate the indoor air quality of houses and apartments in Darkhan city during the winter season using the Purple Air monitoring device. Materials and Methods: A cross-sectional study was conducted with a targeted sample of 128 households in Darkhan city. The study examined factors such as stove type, type of coal used, annual and daily coal consumption, frequency of heating, and chimney sealing conditions. To collect data, the Purple Air monitoring device was installed in each house hold for a month, after which it was retrieved. During retrieval, participants completed a questionnaire. The questionnaire consisted of 55 questions across 7 pages at the time of device installation and 25 questions across 3 pages at the time of device retrieval. The collected data was analyzed using SPSS 25.0. Results: A total of 128 households in Darkhan city participated in the study. The average duration of residence in the current home was 9.5 years, with no statistically significant variation. The distribution of housing types was as follows: traditional Mongolian gers (40.6%), houses (39.1%), and apartments (20.3%). The 24-hour average PM2.5 concentration was highest in gers (70.9 μg/m³), followed by houses (46.8 μg/m³) and apartments (22.8 μg/m³), with a statistically significant difference (p=0.0001). PM2.5 levels were most variable in gers, followed by houses and then apartments. House holds using central heating (apartments) had an average 24-hour PM2.5 concentration of 22.8 μg/m³, whereas households using stoves (gers and houses) had a significantly higher concentration of 59.4 μg/m³ (p=0.0001). However, there was no statistically significant difference between traditional and improved stoves. Among study participants, 21.4% reported that someone in their household smoked indoors. Additionally, 86.5% regularly burned incense, candles, or herbs, while 99.2% did not use an air purifier. Conclusion The indoor particulate matter concentration in houses and gers in Darkhan was 59.4μг/m3. Variations in stove types, poor chimney sealing limited space, and frequent gaps and cracks contribute to increased spread of indoor air pollutants.