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Abstract Background Hair follicle stem cells (HFSCs) typically remain quiescent and are activated only during the transition from telogen to anagen to ensure that the hair follicle enters a new cycle. The metabolic behavior of stem cells in tissues is regulated by macroautophagy/autophagy, and changes in HFSC metabolism directly affect their activation and maintenance. However, the role of autophagy in the regulation of HFSC metabolism and function remains unclear. Methods Back skin samples were obtained from mice at different hair follicle cycle stages, and immunofluorescence staining was used to monitor autophagy in HFSCs. Mouse and human hair follicles were treated with rapamycin (Rapa, an autophagy activator) or 3-methyladenine (3-MA, an autophagy inhibitor). The effects of autophagy on the hair follicle cycle and HFSC were investigated by imaging, cell proliferation staining, and HFSC-specific marker staining. The influence and mechanism of autophagy on HFSC metabolism were explored using RNA sequencing, real-time polymerase chain reaction, immunohistochemical staining, and detection of lactate and glucose concentrations. Finally, the influence of autophagy-induced glycolysis on HFSC and the hair follicle cycle was verified by stem cell characteristics and in vivo functional experiments. Results Autophagy in HFSC was highest during the transition from telogen to anagen. Inhibiting autophagy with 3-MA led to early entry into catagen and prolonged telogen, whereas Rapa promoted autophagy and hair growth. Autophagy activated HFSC by increasing the expression and activity of HFSC lactate dehydrogenase (Ldha), thereby transforming HFSC metabolism into glycolysis. Inhibition of Ldha expression counteracted the effects of autophagy. Conclusions Autophagy activated HFSC by promoting the transition from HFSC metabolism to glycolysis, ultimately initiating the hair follicle cycle and promoting hair growth. Graphical Abstract

BackgroundEpidemics of infectious diseases have a great negative impact on people's daily life. How it changes over time and what kind of laws it obeys are important questions that researchers are always interested in. Among the characteristics of infectious diseases, the phenomenon of recrudescence is undoubtedly of great concern. Understanding the mechanisms of the outbreak cycle of infectious diseases could be conducive for public health policies to the government.MethodIn this study, we collected time-series data for nine class C notifiable infectious diseases from 2009 to 2021 using public datasets from the National Health Commission of China. Oscillatory power of each infectious disease was captured using the method of the power spectrum analysis.ResultsWe found that all the nine class C diseases have strong oscillations, which could be divided into three categories according to their oscillatory frequencies each year. Then, we calculated the oscillation power and the average number of infected cases of all nine diseases in the first 6 years (2009–2015) and the next 6 years (2015–2021) since the update of the surveillance system. The change of oscillation power is positively correlated to the change in the number of infected cases. Moreover, the diseases that break out in summer are more selective than those in winter.ConclusionOur results enable us to better understand the oscillation characteristics of class C infectious diseases and provide guidance and suggestions for the government's prevention and control policies.

BackgroundCOVID-19 was first reported in 2019, and the Chinese government immediately carried out stringent and effective control measures in response to the epidemic. ObjectiveNonpharmaceutical interventions (NPIs) may have impacted incidences of other infectious diseases as well. Potential explanations underlying this reduction, however, are not clear. Hence, in this study, we aim to study the influence of the COVID-19 prevention policies on other infectious diseases (mainly class B infectious diseases) in China. MethodsTime series data sets between 2017 and 2021 for 23 notifiable infectious diseases were extracted from public data sets from the National Health Commission of the People’s Republic of China. Several indices (peak and trough amplitudes, infection selectivity, preferred time to outbreak, oscillatory strength) of each infectious disease were calculated before and after the COVID-19 outbreak. ResultsWe found that the prevention and control policies for COVID-19 had a strong, significant reduction effect on outbreaks of other infectious diseases. A clear event-related trough (ERT) was observed after the outbreak of COVID-19 under the strict control policies, and its decreasing amplitude is related to the infection selectivity and preferred outbreak time of the disease before COVID-19. We also calculated the oscillatory strength before and after the COVID-19 outbreak and found that it was significantly stronger before the COVID-19 outbreak and does not correlate with the trough amplitude. ConclusionsOur results directly demonstrate that prevention policies for COVID-19 have immediate additional benefits for controlling most class B infectious diseases, and several factors (infection selectivity, preferred outbreak time) may have contributed to the reduction in outbreaks. This study may guide the implementation of nonpharmaceutical interventions to control a wider range of infectious diseases.