Jujubes (Ziziphus jujuba Mill), characterized by a rich profile of bioactive compounds, have been historically less exploited due to their unappealing sensory characteristics when dried, including delayed bitterness and a limited shelf life when fresh. Co-fermented jujube puree has emerged as a strategy for enhancing its functional food potential. This study examined the impact of 8-day bicultured Junzao jujube puree, employing both commercial and indigenous Chinese lactic and acetic acid bacteria. Our investigation encompassed an assessment of functionality (cellular profile, antioxidant properties, color, free amino acids, phenolic profiling, volatiles elucidation using headspace-solid phase microextraction gas chromatography–mass spectrometry (HS-SPME-GC-MS), aroma analysis using electronic nose), and microstructural analysis using scanning electron microscopy (SEM). Viable counts of bicultured purees showed probiotic effects exceeding 6–7 log CFU/mL. Strong positive correlations were observed between phenolic compounds (chlorogenic acid, rutin, p-coumaric acid) and antioxidant capacities (ABTS-RSA and DPPH-RSA). The darker color of raw jujube puree was modified, exemplified by a significant (p < 0.05) negative correlation between overall color difference and cyanidin 3-O-rutinoside (R² = −0.768). Purees, particularly those containing bicultures of Lactobacillus helveticus Lh 43 and Acetobacter pasteurianus Ap-As.1.41 HuNiang 1.01 exhibited the highest potential free amino acid content (157.17 ± 1.12 mg/100 g FW) compared to the control (184.03 ± 1.16 mg/100 g FW) with a distinctive formation of L-methionine in biculture of Lactiplantibacillus plantarum Lp 28 and A. pasteurianus Ap-As.1.41 HuNiang 1.01. The phenolic profile of Lacticaseibacillus casei Lc 122 and A. pasteurianus Ap-As.1.41 HuNiang 1.01 increased by 22.79% above the control (48.34 mg/100 g FW) while biculture: L. helveticus Lh 43 and A. pasteurianus Ap-As.1.41 HuNiang 1.01 were enhanced by 4.37%, with the lowest profile in Lp. plantarum Lp 28 and A. pasteurianus Ap-As.1.41 HuNiang 1.01 (46.85 mg/100 g FW). The electronic nose revealed the predominant presence of sulfur, terpenes, and alcohol sensor bioactives in the fermented purees. HS-SPME-GC-MS analysis identified 80 volatile compounds in the bicultured purees, with esters constituting the major group (42%). Furthermore, SEM analysis unveiled massive microscopic alterations in the bicultured purees compared to the unfermented puree. These results collectively demonstrated that lactic–acetic acid co-fermentation serves to biovalorize Junzao jujube puree, enhancing its organoleptic appeal and extending its shelf life.

Mulberry is renowned for its medicinal properties and bioactive compounds, yet its high moisture content renders it highly perishable and challenging to transport over long distances. This inherent limitation to its shelf life poses sustainability challenges due to potential food waste and the increased carbon footprint associated with transportation. To address this issue sustainably, mulberry vinegar emerges as a biotechnological solution. Utilizing a fermented mixture of crushed mulberries, sugar, and mixed acid, transforms the highly perishable raw material into a more stable product. However, conventional methods of mulberry vinegar production often involve heat-intensive processing, which poses environmental concerns and energy inefficiencies. Recognizing the need for sustainable practices, this review delves into alternative non-thermal technologies (NTTs) that can revolutionize mulberry vinegar production. These technologies, such as ultrasonication, ultra-high-pressure homogenization, pulsed light treatments, enzyme-assisted pretreatment, and membrane filtration, offer eco-friendly alternatives by eliminating the need for excessive heat. NTTs enhance energy efficiency and sustainability in mulberry vinegar production by deactivating the microbes and extending the shelf life, thereby enhancing product stability and quality without using thermal methods. Ultrasonication, for example, plays a pivotal role in improving bioactive compound extraction, contributing to the overall quality enhancement of mulberry juice. Enzyme-assisted pretreatment, specifically with Pectinex Ultra SP-L and Viscozyme L, not only enhances juice quality, but also holds promise for sustainable vinegar production. Furthermore, ultra-high-pressure homogenization and pulsed light treatments positively influence mulberry processing, offering additional sustainable alternatives. Membrane filtration, especially ultrafiltration, not only enhances the phenolic content, but also contributes to stability in mulberry juice, showcasing potential benefits for vinegar production. In conclusion, exploring these NTTs represents a transformative shift from traditional heat treatment methods in mulberry food processing. By providing energy efficient, environmentally friendly, and high-quality alternatives, this review offers valuable insights into sustainable practices, particularly in mulberry vinegar production, thereby contributing to a more sustainable future for the mulberry food industry.

Notwithstanding the impact of sustainable innovation on environmental management, its bearing on industrial performance remains hypothetical. Our study seeks to empirically investigate the link between sustainable innovation and industry performance in the United States by employing the generalized method of moments on a nine-year panel spanning from 2014 to 2022. The sample consists of 94 U.S industries, which covers about 7300 companies. The results show that sustainable innovation is not significantly related to industrial performance in the United States. However, it has a moderating effect on industrial output. The Arellano–Bond test, AR (2), confirms the robustness of our findings given the endogeneity assumption and model specifications, and the Hansen test confirms the validity of the instruments. This study expands our knowledge of the link between sustainable innovation and industry performance. A study of this kind is relevant in current times as the United States seeks to attain Sustainable Development Goal 9 by 2030. Further, it provides theoretical guidance on successful environmental management practices to enhance social welfare and maximize output.

Teledermatology expands access to dermatologic expertise in rural settings, yet diagnostic uncertainty persists in low-resource primary care. This retrospective study evaluated MedGemma-4B-IT, a compact multimodal vision-language model, as adjunctive clinical decision support for challenging diagnostic cases. We analyzed 77 zero-concordance cases (360 clinical photographs) from a Dermatology Extension for Community Healthcare Outcomes (ECHO) tele-mentoring program (2016-2021). Zero-concordance cases showed no overlap between primary clinician provisional diagnosis and dermatologist-confirmed diagnosis. The model was prompted using dermatologist-style format to generate ranked differential diagnoses. Performance was assessed using strict case-level top-k exact-match accuracy and relaxed matching criteria based on fuzzy string similarity. MedGemma achieved 0.0% strict top-1 accuracy, 1.3% top-3 accuracy, 3.9% top-5 accuracy, and 3.9% top-10 accuracy. Relaxed concept-level matching achieved 28.6% top-1, 63.6% top-5, and 67.5% top-10 accuracy. Image-level accuracy was 44.2% (159/360, 95% CI 39.0-49.5%). The model surfaced the correct diagnosis within differential lists in 45.5% of cases despite no exact top-1 matches, suggesting utility for differential expansion rather than definitive diagnosis. Performance varied across diagnostic categories, with highest accuracy in Other categories (54.5%) and lowest in neoplastic conditions (0.0%). Common errors included confusion between inflammatory and other diagnostic groupings. These findings characterize MedGemma performance on real-world teledermatology cases and inform safe, clinician-in-the-loop integration into teledermatology workflows where specialist oversight remains essential. What this Study Adds: This study provides empirical evaluation of MedGemma-4B-IT as adjunctive decision support for challenging teledermatology cases in a community healthcare ECHO setting. We demonstrate that while strict top-1 diagnostic accuracy is 0%, the model correctly surfaces the dermatologist-confirmed diagnosis within a 10-item differential in 45.5% of zero-concordance cases, suggesting value as a differential diagnostic prompt rather than a direct diagnostic replacement. These findings inform safe, clinician-in-the-loop deployment strategies for compact vision-language models in resource-limited telemedicine settings. Conclusions: MedGemma demonstrates differential diagnostic utility in challenging teledermatology cases, surfacing the correct diagnosis within a 10-item differential in nearly half of cases despite zero top-1 accuracy. These findings support clinician-in-the-loop AI deployment for diagnostic expansion in resource-limited settings, while highlighting the need for improved neoplastic detection and confidence calibration in future model development. Results: Under strict exact matching, top-1 accuracy was 0.0% (0/77), increasing to 3.9% (3/77) at top-10. Under relaxed concept-level matching, top-1 accuracy was 28.6% (22/77), rising to 45.5% (35/77) at top-10. The Mean Reciprocal Rank was 0.4287. Diagnostic performance varied by category: Other diagnoses showed 54.5% top-10 accuracy, while neoplastic conditions showed 0.0%. Methods: This retrospective study analyzed 77 zero-concordance cases (360 images) from the Missouri Dermatology ECHO program (2016-2021). Cases were those where the primary care clinician provisional diagnosis showed no textual overlap with the dermatologist-confirmed diagnosis. Primary outcome was top-1 exact-match accuracy; secondary outcomes included top-k accuracy under concept-level relaxed matching. Background: Teledermatology expands access to dermatologic expertise in rural settings, yet diagnostic uncertainty persists in low-resource primary care. We evaluated MedGemma-4B-IT, a compact multimodal vision-language model, as adjunctive clinical decision support for challenging teledermatology cases. - Stratified performance analysis by diagnostic category and image count provides actionable guidance for deployment scenarios. - Concept-level relaxed matching reveals clinically relevant differential diagnostic utility that strict exact-match metrics obscure. - Zero-concordance cases provide a rigorous test of model performance, representing the diagnostic frontier where clinical decision support is most needed.