Citations

Biomaterials like gold nanorod-embedded gelatin methacrylate (GelMA-GNR) hydrogels support the delivery of regenerative cells such as human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs), enhancing EHT performance.

Abstract Purpose Lattice radiation therapy (LRT) is a form of spatially fractionated radiation therapy that allows increased total dose delivery aiming for improved treatment response without an increase in toxicities, commonly utilized for palliation of bulky tumors. The LRT treatment planning process is complex, while eligible patients often have an urgent need for expedited treatment start. In this study, we aimed to develop a simulation‐free workflow for volumetric modulated arc therapy (VMAT)‐based LRT planning via deep learning‐predicted synthetic CT (sCT) to expedite treatment initiation. Methods Two deep learning models were initially trained using 3D U‐Net architecture to generate sCT from diagnostic CTs (dCT) of the thoracic and abdomen regions using a training dataset of 50 patients. The models were then tested on an independent dataset of 15 patients using image similarity analysis assessing mean absolute error (MAE) and structural similarity index measure (SSIM) as metrics. VMAT‐based LRT plans were generated based on sCT and recalculated on the planning CT (pCT) for dosimetric accuracy comparison. Differences in dose volume histogram (DVH) metrics between pCT and sCT plans were assessed using the Wilcoxon signed‐rank test. Results The final sCT prediction model demonstrated high image similarity to pCT, with a MAE and SSIM of 38.93 ± 14.79 Hounsfield Units (HU) and 0.92 ± 0.05 for the thoracic region, and 73.60 ± 22.90 HU and 0.90 ± 0.03 for the abdominal region, respectively. There were no statistically significant differences between sCT and pCT plans in terms of organ‐at‐risk and target volume DVH parameters, including maximum dose (Dmax), mean dose (Dmean), dose delivered to 90% (D90%) and 50% (D50%) of target volume, except for minimum dose (Dmin) and (D10%). Conclusion With demonstrated high image similarity and adequate dose agreement between sCT and pCT, our study is a proof‐of‐concept for using deep learning predicted sCT for a simulation‐free treatment planning workflow for VMAT‐based LRT.

Abstract Background Real-world first-line maintenance (1LM) treatment data are limited for advanced/metastatic non-small cell lung cancer (a/mNSCLC). Materials and Methods In this electronic health record-derived, deidentified database study, eligible patients (≥18 years; diagnosed with stage III/IV non-small cell lung cancer [June 1, 2017-September 30, 2021]) initiated pembrolizumab-based 1LM after 4-6 cycles of first-line (1L) platinum-based chemotherapy-pembrolizumab ± pemetrexed. Study outcomes were real-world time to next treatment or death (rwTTNTD), overall survival (rwOS), and predictors of outcomes. Results Of 1944 patients analyzed (median follow-up, 12.2 months), 51.9% received 1LM pembrolizumab-pemetrexed and 48.1% pembrolizumab monotherapy. Median rwTTNTD and rwOS were 9.2 (95% CI: 8.5-9.8) and 18.7 (95% CI: 17.7-20.3) months, respectively. In multivariable analyses, factors significantly associated with shorter rwTTNTD included 5%-<10% (hazard ratio [HR], 1.34; 95% CI: 1.17-1.54) or ≥10% (HR, 1.69; 95% CI: 1.42-2.01) weight loss during 1L versus 0% or <5% weight loss. Programmed death-ligand 1 (PD-L1) expression 1%–49% (HR, 0.81; 95% CI: 0.71-0.93) or ≥50% (HR, 0.55; 95% CI: 0.47-0.64) and female sex (HR, 0.85; 95% CI: 0.75-0.95) were significantly associated with longer rwTTNTD. These variables were also significantly associated with shorter (weight loss 5%-<10%: HR, 1.52; 95% CI: 1.30-1.77; ≥10% HR, 2.06; 95% CI: 1.71-2.48) and longer rwOS (PD-L1 expression: 1%-49% HR, 0.84; 95% CI: 0.72-0.98; ≥ 50% HR, 0.57; 95% CI: 0.48-0.68; female sex: HR, 0.81; 95% CI: 0.71-0.92). Conclusions Predictors of real-world clinical outcomes included 1L treatment, weight loss, PD-L1 status, and sex. Poor outcomes persisted despite immunotherapy-based 1LM availability, revealing an unmet need in this population.

Background/Objectives: The accurate delineation of primary tumors (GTVp) and metastatic lymph nodes (GTVn) in head and neck (HN) cancers is essential for effective radiation treatment planning, yet remains a challenging and laborious task. This study aims to develop a deep-learning-based auto-segmentation (DLAS) model trained on external datasets with false-positive elimination using clinical diagnosis reports. Methods: The DLAS model was trained on a multi-institutional public dataset with 882 cases. Forty-four institutional cases were randomly selected as the external testing dataset. DLAS-generated GTVp and GTVn were validated against clinical diagnosis reports to identify false-positive and false-negative segmentation errors using two large language models: ChatGPT-4 and Llama-3. False-positive ruling out was conducted by matching the centroids of AI-generated contours with the slice locations or anatomical regions described in the reports. Performance was evaluated using the Dice similarity coefficient (DSC), 95th percentile Hausdorff distance (HD95), and tumor detection precision. Results: ChatGPT-4 outperformed Llama-3 in accurately extracting tumor locations from the diagnostic reports. False-positive contours were identified in 15 out of 44 cases. The DSCmean of the DLAS contours for GTVp and GTVn increased from 0.68 to 0.75 and from 0.69 to 0.75, respectively, after the ruling-out process. Notably, the average HD95 value for GTVn decreased from 18.81 mm to 5.2 mm. Post ruling out, the model achieved 100% precision for GTVp and GTVn when compared with the results of physician-determined contours. Conclusions: The false-positive ruling-out approach based on diagnostic reports effectively enhances the precision of DLAS in the HN region. The model accurately identifies the tumor location and detects all false-negative errors.

Abstract Background and Aims Visceral pain is a cardinal symptom of many disorders affecting the gut. Modulators of gamma-aminobutyric acid (GABA) such as benzodiazepines may attenuate colonic pain but the specific contribution of peripheral GABAA receptors remains unclear as these agents have prominent central effects. Methods Using medicinal chemistry optimization of the benzodiazepine scaffold, we developed a novel and potent benzodiazepine-based positive allosteric modulator (PAM) of GABAA receptors, Li633, with no significant central nervous system (CNS) penetration. Results The locomotor activity of rats placed in an open field was unchanged with Li633 at doses up to 30 mg/kg, confirming its lack of a CNS effect. LI-633 produced robust potentiation of GABA-induced inward current, with EC50 values ranging from 8 nM (α5β2γ2) to 128 nM (α3β2γ2). In vitro electrophysiological studies confirmed its ability to reduce excitability of human dorsal root ganglion (DRG) neurons. LI-633 potentiated muscimol-induced GABAergic currents in rat DRG neurons in a dose-dependent manner, with an EC 50 of 70.4 nM. In vivo, LI-633 significantly attenuated visceral hypersensitivity and pain behavior in a rat model of irritable bowel syndrome (IBS) and functional dyspepsia (FD). In the IBS model, administration of the drug also resulted in decreased excitability of colon-specific DRG neurons and significantly reduced the colonic afferent response to balloon distention as measured by recordings of neural activity in dorsal ganglia rootlets. Conclusions These findings highlight the potential of targeting peripheral GABAA receptors for pain management in IBS and other disorders associated with visceral hypersensitivity.

Tryptophan hydroxylase (TPH) is a rate-limiting enzyme for serotonin or 5-hydroxytryptamine (5-HT) synthesis. Previously, adipocyte TPH1 has been linked to increased adipose 5-HT, reduced brown adipose tissue (BAT) thermogenesis, and obesity. However, the role of TPH2, a neural isoform highly expressed in obese adipose tissue, is unknown. Here, we report that adipose tissue expression of TPH2 is dramatically elevated in mice with diet-induced obesity (DIO) and ob/ob mice, as well as in obese humans. In mice fed a high-fat diet, adipocyte TPH2 deficiency improved DIO-induced metabolic complications, enhanced BAT thermogenesis, and increased intestinal energy-harvesting efficiency without affecting adiposity. Conversely, TPH2 overexpression in epididymal adipocytes of chow-fed mice raised adipose and plasma 5-HT levels, suppressed BAT thermogenesis, and exacerbated obesity and metabolic dysfunction. We found that obesity-induced hyperinsulinemia upregulated adipocyte TPH2 expression via activation of mechanistic target of rapamycin complex 1 and SREBP1. In humans, TPH2 mRNA levels in subcutaneous adipose tissue, but not those of TPH1 , are positively correlated with fasting plasma insulin concentrations. In summary, our study demonstrates that obesity-associated increases in adipocyte TPH2 can regulate distal tissue physiology and energy metabolism, suggesting that TPH2 could be a potential therapeutic target for obesity and its associated complications.