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This study assessed the cost-effectiveness of different diabetic retinopathy (DR) screening strategies in rural regions in China by using a Markov model to make health economic evaluations. In this study, we determined the structure of a Markov model according to the research objectives, which required parameters collected through field investigation and literature retrieval. After perfecting the model with parameters and assumptions, we developed a Markov decision analytic model according to the natural history of DR in TreeAge Pro 2011. For this model, we performed Markov cohort and cost-effectiveness analyses to simulate the probabilistic distributions of different developments in DR and the cumulative cost-effectiveness of artificial intelligence (AI)-based screening and ophthalmologist screening for DR in the rural population with diabetes mellitus (DM) in China. Additionally, a model-based health economic evaluation was performed by using quality-adjusted life years (QALYs) and incremental cost-effectiveness ratios. Last, one-way and probabilistic sensitivity analyses were performed to assess the stability of the results. From the perspective of the health system, compared with no screening, AI-based screening cost more (the incremental cost was 37,257.76 RMB (approximately 5,211.31 US dollars)), but the effect was better (the incremental utility was 0.33). Compared with AI-based screening, the cost of ophthalmologist screening was higher (the incremental cost was 14,886.76 RMB (approximately 2,070.19 US dollars)), and the effect was worse (the incremental utility was -0.31). Compared with no screening, the incremental cost-effectiveness ratio (ICER) of AI-based DR screening was 112,146.99 RMB (15,595.47 US dollars)/QALY, which was less than the threshold for the ICER (< 3 times the per capita gross domestic product (GDP), 217,341.00 RMB (30,224.03 US dollars)). Therefore, AI-based screening was cost-effective, which meant that the increased cost for each additional quality-adjusted life year was merited. Compared with no screening and ophthalmologist screening for DR, AI-based screening was the most cost-effective, which not only saved costs but also improved the quality of life of diabetes patients. Popularizing AI-based DR screening strategies in rural areas would be economically effective and feasible and can provide a scientific basis for the further formulation of early screening programs for diabetic retinopathy.

Most reported risk factors for developmental speech delay (DSD) remain controversial, and studies on paternal influencing factors are rare. This study investigated family environmental risk factors for DSD in northern China. The medical records of 276 patients diagnosed with DSD at four centres between October 2018 and October 2019 were retrospectively analysed. A questionnaire was designed that contained items such as maternal age at the child’s birth, child sex, child age, birth order, family type and parental personality. Patients whose medical records lacked complete information for this investigation were contacted by e-mail or phone. Additionally, 339 families whose children received routine physical examinations at the four involved centres completed the survey. Data were collected, and potential risk factors were analysed using the t test or chi-square test; the obtained outcomes were subjected to multivariable logistic regression for further analysis. The multivariable regression showed that older maternal age at the child’s birth (OR = 1.312 (1.192–1.444), P < 0.001), introverted paternal personality (OR = 0.023 (0.011–0.048), P < 0.001), low average parental education level (OR = 2.771 (1.226–6.263), P = 0.014), low monthly family income (OR = 4.447 (1.934–10.222), P < 0.001), and rare parent–child communication (OR = 6.445 (3.441–12.072), P < 0.001) were independent risk factors for DSD in children in North China. The study results may provide useful data for broadening and deepening the understanding of family risk factors for DSD.

Although numerous studies have described the application of artificial intelligence (AI) in diabetic retinopathy (DR) screening among diabetic populations, studies among populations in rural areas are rare. The purpose of this study was to evaluate the application value of an AI-based diagnostic system for DR screening in rural areas of midwest China. In this diagnostic accuracy study, diabetes mellitus (DM) patients in the National Basic Public Health Information Systems of Licheng County and Lucheng County of Changzhi city from July to December 2020 were selected as the target population. A total of 7824 eyes of 3933 DM patients were enrolled in this screening; the patients included 1395 males and 2401 females, with an average age of 19-87 years (63±8.735 years). All fundus photographs were collected by a professional ophthalmologist under natural pupil conditions in a darkroom using the Zhiyuan Huitu fundus image AI analysis software EyeWisdom. The AI-based diagnostic system and ophthalmologists were tasked with diagnosing the photos independently, and the consistency rate, sensitivity and specificity of the two methods in diagnosing DR were calculated and compared. The prevalence rates of DR according to the ophthalmologist and AI diagnoses were 22.7% and 22.5%, respectively; the consistency rate was 81.6%. The sensitivity and specificity of the AI system relative to the ophthalmologists' grades were 81.2% (95% confidence interval [CI]: 80.3% 82.1%) and 94.3% (95% CI: 93.7% 94.8%), respectively. There was no significant difference in diagnostic outcomes between the methods (X2 = 0.329, P = 0.566, P>0.05), and the AI-based diagnostic system had high consistency with the ophthalmologists' diagnostic results ([kappa] = 0.752). Our research demonstrated that DR patients in rural area hospitals can be screened feasibly. Compared with that of the ophthalmologists, however, the accuracy of the AI system must be improved. The results of this study might lend support to the large-scale application of AI in DR screening among different populations.

Background Although numerous studies have described the application of artificial intelligence (AI) in diabetic retinopathy (DR) screening among diabetic populations, studies among populations in rural areas are rare. The purpose of this study was to evaluate the application value of an AI-based diagnostic system for DR screening in rural areas of midwest China. Methods In this diagnostic accuracy study, diabetes mellitus (DM) patients in the National Basic Public Health Information Systems of Licheng County and Lucheng County of Changzhi city from July to December 2020 were selected as the target population. A total of 7824 eyes of 3933 DM patients were enrolled in this screening; the patients included 1395 males and 2401 females, with an average age of 19–87 years (63±8.735 years). All fundus photographs were collected by a professional ophthalmologist under natural pupil conditions in a darkroom using the Zhiyuan Huitu fundus image AI analysis software EyeWisdom. The AI-based diagnostic system and ophthalmologists were tasked with diagnosing the photos independently, and the consistency rate, sensitivity and specificity of the two methods in diagnosing DR were calculated and compared. Results The prevalence rates of DR according to the ophthalmologist and AI diagnoses were 22.7% and 22.5%, respectively; the consistency rate was 81.6%. The sensitivity and specificity of the AI system relative to the ophthalmologists’ grades were 81.2% (95% confidence interval [CI]: 80.3% 82.1%) and 94.3% (95% CI: 93.7% 94.8%), respectively. There was no significant difference in diagnostic outcomes between the methods (χ2 = 0.329, P = 0.566, P>0.05), and the AI-based diagnostic system had high consistency with the ophthalmologists’ diagnostic results (κ = 0.752). Conclusion Our research demonstrated that DR patients in rural area hospitals can be screened feasibly. Compared with that of the ophthalmologists, however, the accuracy of the AI system must be improved. The results of this study might lend support to the large-scale application of AI in DR screening among different populations.

This study assessed the cost-effectiveness of different diabetic retinopathy (DR) screening strategies in rural regions in China by using a Markov model to make health economic evaluations. In this study, we determined the structure of a Markov model according to the research objectives, which required parameters collected through field investigation and literature retrieval. After perfecting the model with parameters and assumptions, we developed a Markov decision analytic model according to the natural history of DR in TreeAge Pro 2011. For this model, we performed Markov cohort and cost-effectiveness analyses to simulate the probabilistic distributions of different developments in DR and the cumulative cost-effectiveness of artificial intelligence (AI)-based screening and ophthalmologist screening for DR in the rural population with diabetes mellitus (DM) in China. Additionally, a model-based health economic evaluation was performed by using quality-adjusted life years (QALYs) and incremental cost-effectiveness ratios. Last, one-way and probabilistic sensitivity analyses were performed to assess the stability of the results. From the perspective of the health system, compared with no screening, AI-based screening cost more (the incremental cost was 37,257.76 RMB (approximately 5,211.31 US dollars)), but the effect was better (the incremental utility was 0.33). Compared with AI-based screening, the cost of ophthalmologist screening was higher (the incremental cost was 14,886.76 RMB (approximately 2,070.19 US dollars)), and the effect was worse (the incremental utility was -0.31). Compared with no screening, the incremental cost-effectiveness ratio (ICER) of AI-based DR screening was 112,146.99 RMB (15,595.47 US dollars)/QALY, which was less than the threshold for the ICER (< 3 times the per capita gross domestic product (GDP), 217,341.00 RMB (30,224.03 US dollars)). Therefore, AI-based screening was cost-effective, which meant that the increased cost for each additional quality-adjusted life year was merited. Compared with no screening and ophthalmologist screening for DR, AI-based screening was the most cost-effective, which not only saved costs but also improved the quality of life of diabetes patients. Popularizing AI-based DR screening strategies in rural areas would be economically effective and feasible and can provide a scientific basis for the further formulation of early screening programs for diabetic retinopathy.

The Strait of Malacca （SoM）, the world＇s busiest sea-route, is increasingly polluted as the rapid develop- ment of world trades, affecting phytoplankton primary productivity therein. The variations of surface phy- toplankton biomass, size-structure and carbon fixation were investigated across the SoM during the spring period （May 4 to 9, 2011）. Chlorophyll a concentration increased from 0.12 ptg/L at the northwest entrance of the SoM to a maximal 0.63 #g/L at narrowest section, and decreased to 0.10/.tg/L at the southeast entrance. Photosynthetic carbon fixation by phytoplankton coincided well with Chl a biomass, and increased from 10.8 to 22.3 pg C/（L.d）, then decreased to 9.21/zg C/（L.d）; while the carbon fixation rate showed an inverse pattern to the changes of Chl a, and decreased from 87.1 to 35.5 #g C/（#g Chl a.d） and increased thereafter to 95.3 btg C/（/2g Chl a.d）. Picophytoplankton cells （〈3/2m） contributed to more than 60% and 50% of the total Chl a and carbon fixation at both the entry waters; while the contributions of pico-cells decreased sharply to the minimum of 18.3% and 27.5% at the narrowest part of the SoM. In particular, our results showed that the silicate concentration positively regulated Chl a biomass and carbon fixation, reflecting that the higher silicate favoured the growth of phytoplankton and thus led to higher primary production in this strait.

HA/TCP and HA rods （φ5 mm×10 mm） were made for implantation in New Zealand white rabbit with different condition. Sixty three rabbit were divided into three groups： group 1 （n=18）, group 2 （n=27） and group 3 （n=18）. In group 1, 10 mm radius was defected, and one HA/TCP rod was implanted in the muscle a distant away from the bone defect area. In group 2, also, 10 mm radius was defected, one HA rod was implanted in the muscle a distant away from the bone defect area. In group 3, two HA/TCP rods were implanted in the dorsal muscle of the rabbit with bone intact. Histological observation showed that in group 1, some new bone was found only two months after implantation （n=2）, and obvious immature woven bone could be observed in these bioceramics from the 3rd month on. However, in group 3, bone began to be found 6 months after implantation （n=2）. In group 2, we could not find any bone tissue up to 9 month’s observation. These results suggest that, first, the bone defect model could significantly accelerate bone formation at non-osseous sites in rabbits; second,. HA/TCP bioceramics were confirmed with osteoinductive property while HA bioceramics without osteoinductive property nearly. Thus, bone defect might be a good animal model for further researches for osteoinductive bioceramics.

Achieving rapid and scar-free wound repair is a key goal in the field of regenerative medicine. Herein, a dynamically Schiff base-crosslinked hydrogel (F/R gel) with phase-adaptive regulating functions is constructed to integratedly promote rapid re-epithelization with suppressed scars on chronic infected wounds. Specifically, the gel effectively eliminates multidrug-resistant bacterial biofilm at infection stage via antimicrobial activity of ε-polylysine firstly dissociated from hydrogel matrix in infectious microenvironment, and interrupts the severe oxidative stress-inflammation cycle at wound site by the released ceria nanozyme, thus stimulating a pro-regenerative environment to ensure tissue repair. Subsequently, fibroblast growth factor/ c-Jun siRNA co-loaded microcapsules gradually disintegrate to release drugs, facilitating neoangiogenesis and cell proliferation but simultaneously blocking c-Jun overexpression for fibrotic scar suppression. Notably, the F/R gel facilitates normal-like skin regeneration with no perceptible scars formed on infected male mouse wound and female rabbit ear wound models. Our work offers a promising regenerative strategy emphasizing immunomodulatory and fibroblast subtype modulation for scarless wound repair. It is challenging to achieve scar-free repair of chronic wounds as they often feature the occurrence of multiple healing phases in an unpredictable and nonlinear manner. Here, the authors report a healing phase-adaptive regulating hydrogel that exhibits hierarchically delivering performance for programmed modulation of chronic infected wounds.

This paper investigates a discrete conformable fractional order bacteria population model in a microcosm. When the parameters of this discrete model satisfy some conditions, its positive fixed point has a double eigenvalue - 1 with geometric multiplicity 1, corresponding to a 1:2 strong resonance. Applying the normal form theory and the approximation theory by a flow, we firstly transform this discrete model into a two-dimensional ordinary differential system. Then we use the bifurcation theory to analyze the qualitative properties of this ordinary differential system near the degenerate equilibrium. Therefore, the qualitative properties of the corresponding fixed point are obtained, which show that this discrete model undergoes the period-doubling bifurcation, the Neimark-Sacker bifurcation, the homoclinic bifurcation and the double closed orbit bifurcation. We also add a hybrid controller to this model such that the fixed point is locally asymptotically stable. Finally, numerical simulations are given to verify the theoretical results.

The foreign body response (FBR) to implanted biomaterials and biomedical devices can severely impede their functionality and even lead to failure. The discovery of effective anti‐FBR materials remains a formidable challenge. Inspire by the enrichment of glutamic acid (E) and lysine (K) residues on human protein surfaces, a class of zwitterionic polypeptide (ZIP) hydrogels with alternating E and K sequences to mitigate the FBR is prepared. When subcutaneously implanted, the ZIP hydrogels caused minimal inflammation after 2 weeks and no obvious collagen capsulation after 6 months in mice. Importantly, these hydrogels effectively resisted the FBR in non‐human primate models for at least 2 months. In addition, the enzymatic degradability of the gel can be controlled by adjusting the crosslinking degree or the optical isomerism of amino acid monomers. The long‐term FBR resistance and controlled degradability of ZIP hydrogels open up new possibilities for a broad range of biomedical applications. Inspired by the enrichment of glutamic acid (E) and lysine (K) residues on human protein surfaces, a class of zwitterionic polypeptide (ZIP) hydrogels with alternating E and K sequences to mitigate the FBR is prepared. When subcutaneously implanted, the ZIP hydrogels causes minimal inflammation and no obvious collagen capsulation in mice and non‐human primates.