Thirty years ago, Northern Bohemia in the Czech Republic was one of the most air polluted areas in Europe. After political changes, the Czech government put forward a research program to determine if air pollution is really affecting human health. This program, later called the "Teplice Program", was initiated in collaboration with scientists from the United States Environmental Protection Agency (US EPA). This cooperation made possible the use of methods on the contemporary level. The very high concentrations of sulphur dioxide (SO2), particulate matter of 10 micrometers or less (PM10), and polycyclic aromatic hydrocarbons (PAHs) present in the air showed, for the first time, the impact of air pollutants on the health of the population in mining districts adverse pregnancy outcomes, the impact of air pollution on sperm morphology, learning disabilities in children, and respiratory morbidity in preschool children. A surprising result came from the distribution of the sources of pollution 70% of PM10 pollution came from local heating and not from power plants as expected. Thanks to this result, the Czech government supported changes in local heating from brown coal to natural gas. This change substantially decreased SO2 and PM10 pollution and affected mortality, especially cardiovascular mortality.Although it is known that the gut microbiota (GM) can be modulated by diet, the efficacy of specific dietary interventions in determining its composition and diversity in obese patients remains to be ascertained. The present work aims to evaluate the impact of a moderately hypocaloric Mediterranean diet on the GM of obese and overweight patients (OB). The GM of 23 OB patients (F/M = 20/3) was compared before (T0) and after 3 months (T3) of nutritional intervention (NI). Fecal samples were analyzed by Illumina MiSeq sequencing of the 16S rRNA gene. At baseline, GM characterization confirmed typical obesity-associated dysbiosis. After 3 months of NI, patients presented a statistically significant reduction in body weight and fat mass, along with changes in the relative abundance of many microbial patterns. In fact, an increase in the abundance of several Bacteroidetes taxa (i.e., Sphingobacteriaceae, Sphingobacterium, Bacteroides spp., Prevotella stercorea) and a depletion of many Firmicutes taxa (i.e., Lachnospiraceae members, Ruminococcaceae and Ruminococcus, Veillonellaceae, Catenibacterium, Megamonas) were observed. In addition, the phylum Proteobacteria showed an increased abundance, while the genus Sutterella, within the same phylum, decreased after the intervention. Metabolic pathways, predicted by bioinformatic analyses, showed a decrease in membrane transport and cell motility after NI. The present study extends our knowledge of the GM profiles in OB, highlighting the potential benefit of moderate caloric restriction in counteracting the gut dysbiosis.
  Experimental studies using qualitative or quantitative analysis have demonstrated that the human voice progressively worsens with ageing. These studies, however, have mostly focused on specific voice features without examining their dynamic interaction. To examine the complexity of age-related changes in voice, more advanced techniques based on machine learning have been recently applied to voice recordings but only in a laboratory setting. We here recorded voice samples in a large sample of healthy subjects. To improve the ecological value of our analysis, we collected voice samples directly at home using smartphones.
 
  138 younger adults (65 males and 73 females, age range 15-30) and 123 older adults (47 males and 76 females, age range 40-85) produced a sustained emission of a vowel and a sentence. The recorded voice samples underwent a machine learning analysis through a support vector machine algorithm.
 
  The machine learning analysis of voice samples from both speech tasks discriminated between younger and older adults, and between males and females, with high statistical accuracy.
 
  By recording voice samples through smartphones in an ecological setting, we demonstrated the combined effect of age and gender on voice. Our machine learning analysis demonstrates the effect of ageing on voice.
 By recording voice samples through smartphones in an ecological setting, we demonstrated the combined effect of age and gender on voice. Our machine learning analysis demonstrates the effect of ageing on voice.The emergence of the Coronavirus Disease 2019 (COVID-19) caused by the SARS-CoV-2 virus has led to an unprecedented pandemic, which demands urgent development of antiviral drugs and antibodies; as well as prophylactic approaches, namely vaccines. Algae biotechnology has much to offer in this scenario given the diversity of such organisms, which are a valuable source of antiviral and anti-inflammatory compounds that can also be used to produce vaccines and antibodies. Antivirals with possible activity against SARS-CoV-2 are summarized, based on previously reported activity against Coronaviruses or other enveloped or respiratory viruses. Moreover, the potential of algae-derived anti-inflammatory compounds to treat severe cases of COVID-19 is contemplated. The scenario of producing biopharmaceuticals in recombinant algae is presented and the cases of algae-made vaccines targeting viral diseases is highlighted as valuable references for the development of anti-SARS-CoV-2 vaccines. Successful cases in the production of functional antibodies are described. Perspectives on how specific algae species and genetic engineering techniques can be applied for the production of anti-viral compounds antibodies and vaccines against SARS-CoV-2 are provided.A temperature sensor was fabricated with a functional conductive poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOTPSS) coating on a long-period fiber grating (LPFG). The LPFG was fabricated by laser-assisted wet-chemical etching for controlling the grating depth of the LPFG after the treated surface of an optical fiber was inscribed by laser light. The functional conductive polymer acts as a temperature sustained sensing layer and enhances the grating depth of the LPFG sensor as a strain buffer at various temperatures. The sensor was subjected to three cycles of temperature measurement to investigate the sensor's wavelength shift and energy loss when exposed to temperatures between 30 and 100 °C.  https://www.selleckchem.com/products/nicotinamide-riboside-chloride.html  Results showed that the sensor's average wavelength sensitivity and its linearity were 0.052 nm/°C and 99%, respectively; average transmission sensitivity and linearity were 0.048 (dB/°C) and 95%, respectively.