12 research outputs found
First Report of Insect Endophytic Oviposition from the Upper Permian of the Pechora Basin, on a Leaf of Phylladoderma (Peltaspermopsida: Cardiolepidaceae)
First Report of Insect Endophytic Oviposition from the Upper Permian of the Pechora Basin, on a Leaf of Phylladoderma (Peltaspermopsida: Cardiolepidaceae)
Β© 2020, Pleiades Publishing, Ltd. Abstract: The first endophytic oviposition from the Upper Permian of the Pechora Basin (Talbeyskaya Formation, SeverodvinianβVyatkian) is described in a formal system. Paleoovoidus krassilovi sp. nov. is a linear oviposition arranged in two oppositely directed rows on a leaf of Phylladoderma arberi Zalessky, 1913; it was probably produced by an odonatan insect. Cuticle punctures that probably represent traces of feeding by small and/or young palaeodictyopteroid nymphs were previously described from Phylladoderma leaves found in the same deposits. Fossil insects remain unknown from the Talbeyskaya Formation, but fossil records of plantβinsect interactions enable the reconstruction of a well-balanced insect community that included sucking phytophages (palaeodictyopteroids) and predators (odonatans)
ΠΠ΅ΠΉΡΡΠΎΡΠΈΠ»ΡΠ½ΡΠ΅ Π²Π½Π΅ΠΊΠ»Π΅ΡΠΎΡΠ½ΡΠ΅ Π»ΠΎΠ²ΡΡΠΊΠΈ: Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅ Π΄Π»Ρ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ ΠΈ ΠΏΡΠΎΠ³Π½ΠΎΠ·Π° COVID-19
Rationale: An important element of antiviral defense in the pathophysiology of COVID-19 is the innate cell immunity including polymorphonuclear neutrophils prone to netotic transformation. Neutrophils can be not only a marker of acute infection, but, being a source of neutrophil extracellular traps (NET), can play a key role in the development of thrombotic complications leading to acute respiratory insufficiency in COVID-19.Aim: To determine the diagnostic and prognostic value of NET levels in patients with COVID-19.Materials and methods: We monitored NET levels in peripheral blood of 34 patients with COVID-19 (mean age, 67 Β± 15.8 years), admitted to MONIKI hospital. The control group consisted of 54 healthy volunteers (mean age, 52 Β± 11.5 years). Whole blood samples of 2 pL each were used for the preparation of monolayer smears (Giemsa stain) and calculation of at least 200 cell structures including native intact and transformed neutrophils (MECOS-C2 microscope, Medical computer systems).Results: Patients with COVID-19 had higher NET levels, compared to those in healthy controls: 14.5% (2.9-28.6%) vs. 5.0% (1.8-11.9%, p 0.0001). The patients who were on non-invasive respiratory support (23.5%) had a NET level of 12% (8.122.3%), whereas those on invasive mechanical ventilation (17.6%) had a 1.5-fold higher NET level of 17.9% (12.3-28.2%) (p 0.05). In the patients who died (11.8% of the cases), the NET level amounted to 19% (16.5-26%, p 0.05). Monitoring of blood NET levels was performed in 9 patients from the day of admittance to the day of their transfer to the intensive care unit / discharge / death. It was shown that a decrease of NET levels mirrors an improvement of the patient's clinical condition and efficacy of his/hers treatment. On the opposite, an increase of NET levels can indicate a deterioration and risk of unfavorable course.Conclusion: We have identified some pathophysiological mechanisms in COVID-19, related to the neutrophil compartment. Patients with coronavirus infection are characterized by high NET levels which is at least 3-fold higher than that in healthy volunteers. This indicates an abnormality in immune host defense and development of an inadequate inflammatory response. An increase of NET in whole blood smears of more than 16% can be a criterion of an unfavorable prognosis of the disease course and the risk of death.ΠΠΊΡΡΠ°Π»ΡΠ½ΠΎΡΡΡ. Π ΠΏΠ°ΡΠΎΠ³Π΅Π½Π΅Π·Π΅ COVID-19 Π²Π°ΠΆΠ½ΡΠΌ ΡΠ»Π΅ΠΌΠ΅Π½ΡΠΎΠΌ ΠΏΡΠΎΡΠΈΠ²ΠΎΠ²ΠΈΡΡΡΠ½ΠΎΠΉ Π·Π°ΡΠΈΡΡ Π²ΡΡΡΡΠΏΠ°Π΅Ρ Π²ΡΠΎΠΆΠ΄Π΅Π½Π½ΡΠΉ ΠΊΠ»Π΅ΡΠΎΡΠ½ΡΠΉ ΠΈΠΌΠΌΡΠ½ΠΈΡΠ΅Ρ, Π²ΠΊΠ»ΡΡΠ°ΡΡΠΈΠΉ Π² ΡΠΎΠΌ ΡΠΈΡΠ»Π΅ ΠΏΠΎΠ»ΠΈΠΌΠΎΡΡΠ½ΠΎΡΠ΄Π΅ΡΠ½ΡΠ΅ Π½Π΅ΠΉΡΡΠΎΡΠΈΠ»Ρ, ΡΠΊΠ»ΠΎΠ½Π½ΡΠ΅ ΠΊ Π½Π΅ΡΠΎΠ·Π½ΠΎΠΉ ΡΡΠ°Π½ΡΡΠΎΡΠΌΠ°ΡΠΈΠΈ. ΠΡΠΈ ΡΡΠΎΠΌ Π½Π΅ΠΉΡΡΠΎΡΠΈΠ»Ρ Π½Π΅ ΡΠΎΠ»ΡΠΊΠΎ ΡΠ»ΡΠΆΠ°Ρ ΠΌΠ°ΡΠΊΠ΅ΡΠΎΠΌ ΠΎΡΡΡΠΎΠΉ ΠΈΠ½ΡΠ΅ΠΊΡΠΈΠΈ, Π½ΠΎ ΠΈ, Π±ΡΠ΄ΡΡΠΈ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠΌ Π½Π΅ΠΉΡΡΠΎΡΠΈΠ»ΡΠ½ΡΡ
Π²Π½Π΅ΠΊΠ»Π΅ΡΠΎΡΠ½ΡΡ
Π»ΠΎΠ²ΡΡΠ΅ΠΊ (ΠΠΠ), ΠΈΠ³ΡΠ°ΡΡ ΠΊΠ»ΡΡΠ΅Π²ΡΡ ΡΠΎΠ»Ρ Π² ΡΠ°Π·Π²ΠΈΡΠΈΠΈ ΡΡΠΎΠΌΠ±ΠΎΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΎΡΠ»ΠΎΠΆΠ½Π΅Π½ΠΈΠΉ, ΠΏΡΠΈΠ²ΠΎΠ΄ΡΡΠΈΡ
ΠΊ ΠΎΡΡΡΠΎΠΉ Π΄ΡΡ
Π°ΡΠ΅Π»ΡΠ½ΠΎΠΉ Π½Π΅Π΄ΠΎΡΡΠ°ΡΠΎΡΠ½ΠΎΡΡΠΈ ΠΏΡΠΈ COVID-19.Π¦Π΅Π»Ρ - ΡΡΡΠ°Π½ΠΎΠ²ΠΈΡΡ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΎΠ΅ ΠΈ ΠΏΡΠΎΠ³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΎΠ΅ Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅ ΡΡΠΎΠ²Π½Ρ Π²Π½Π΅ΠΊΠ»Π΅ΡΠΎΡΠ½ΡΡ
Π½Π΅ΠΉΡΡΠΎΡΠΈΠ»ΡΠ½ΡΡ
Π»ΠΎΠ²ΡΡΠ΅ΠΊ Ρ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Ρ COVID-19.ΠΠ°ΡΠ΅ΡΠΈΠ°Π» ΠΈ ΠΌΠ΅ΡΠΎΠ΄Ρ. ΠΡΠΎΠ²Π΅Π΄Π΅Π½ ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΠ½Π³ ΡΡΠΎΠ²Π½Ρ ΠΠΠ ΠΏΠ΅ΡΠΈΡΠ΅ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΊΡΠΎΠ²ΠΈ Ρ 34 ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Ρ COVID-19 (ΡΡΠ΅Π΄Π½ΠΈΠΉ Π²ΠΎΠ·ΡΠ°ΡΡ 67 Β± 15,8 Π³ΠΎΠ΄Π°), Π³ΠΎΡΠΏΠΈΡΠ°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
Π² ΠΠΠ£Π ΠΠ ΠΠΠΠΠΠ ΠΈΠΌ. Π.Π€. ΠΠ»Π°Π΄ΠΈΠΌΠΈΡΡΠΊΠΎΠ³ΠΎ. ΠΠΎΠ½ΡΡΠΎΠ»ΡΠ½ΡΡ Π³ΡΡΠΏΠΏΡ ΡΠΎΡΡΠ°Π²ΠΈΠ»ΠΈ 54 ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈ Π·Π΄ΠΎΡΠΎΠ²ΡΡ
Π΄ΠΎΠ±ΡΠΎΠ²ΠΎΠ»ΡΡΠ° (ΡΡΠ΅Π΄Π½ΠΈΠΉ Π²ΠΎΠ·ΡΠ°ΡΡ 52 Β± 11,5 Π³ΠΎΠ΄Π°). ΠΠ· 2 ΠΌΠΊΠ» ΡΠ΅Π»ΡΠ½ΠΎΠΉ ΠΊΡΠΎΠ²ΠΈ Π³ΠΎΡΠΎΠ²ΠΈΠ»ΠΈ ΠΌΠ°Π·ΠΊΠΈ ΠΏΠΎ ΡΠΈΠΏΡ Β«ΠΌΠΎΠ½ΠΎΡΠ»ΠΎΠΉΒ», ΠΎΠΊΡΠ°ΡΠΈΠ²Π°Π»ΠΈ ΠΏΠΎ Π ΠΎΠΌΠ°Π½ΠΎΠ²ΡΠΊΠΎΠΌΡ - ΠΠΈΠΌΠ·Π΅ ΠΈ Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΡΠΈΡΡΠ΅ΠΌΡ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΌΠΈΠΊΡΠΎΡΠΊΠΎΠΏΠ° ΠΠΠΠΠ‘-Π¦2 (ΠΠΠ Β«ΠΠ΅Π΄ΠΈΡΠΈΠ½ΡΠΊΠΈΠ΅ ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡ (ΠΠΠΠΠ‘)Β») ΠΏΠΎΠ΄ΡΡΠΈΡΡΠ²Π°Π»ΠΈ ΠΌΠ°ΡΡΠΈΠ² Π½Π΅ ΠΌΠ΅Π½Π΅Π΅ 200 ΠΊΠ»Π΅ΡΠΎΡΠ½ΡΡ
ΡΡΡΡΠΊΡΡΡ, Π²ΠΊΠ»ΡΡΠ°ΡΡΠΈΠΉ Π½Π°ΡΠΈΠ²Π½ΡΠ΅ Π½Π΅ΡΠ°Π·ΡΡΡΠ΅Π½Π½ΡΠ΅ ΠΈ ΡΡΠ°Π½ΡΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½Π½ΡΠ΅ Π½Π΅ΠΉΡΡΠΎΡΠΈΠ»Ρ.Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ. Π£ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Ρ COVID-19 Π·Π°ΡΠ΅Π³ΠΈΡΡΡΠΈΡΠΎΠ²Π°Π½ Π²ΡΡΠΎΠΊΠΈΠΉ ΡΡΠΎΠ²Π΅Π½Ρ ΠΠΠ - 14,5% (2,9-28,6%) ΠΏΡΠΎΡΠΈΠ² 5,0% (1,8-11,9%) Π² ΠΊΠΎΠ½ΡΡΠΎΠ»ΡΠ½ΠΎΠΉ Π³ΡΡΠΏΠΏΠ΅ (p 0,0001). 23,5% Π±ΠΎΠ»ΡΠ½ΡΡ
, ΠΏΠΎΠ»ΡΡΠ°Π²ΡΠΈΡ
ΠΏΡΠΎΡΡΡΡ ΡΠ΅ΡΠΏΠΈΡΠ°ΡΠΎΡΠ½ΡΡ ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΡ, ΠΏΡΠΎΠ΄Π΅ΠΌΠΎΠ½ΡΡΡΠΈΡΠΎΠ²Π°Π»ΠΈ ΡΡΠΎΠ²Π΅Π½Ρ ΠΠΠ 12% (8,1-22,3%), ΡΠΎΠ³Π΄Π° ΠΊΠ°ΠΊ Ρ 17,6% ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ², ΠΊΠΎΡΠΎΡΡΠ΅ Π±ΡΠ»ΠΈ ΠΏΠΎΠ΄ΠΊΠ»ΡΡΠ΅Π½Ρ ΠΊ ΠΈΡΠΊΡΡΡΡΠ²Π΅Π½Π½ΠΎΠΉ Π²Π΅Π½ΡΠΈΠ»ΡΡΠΈΠΈ Π»Π΅Π³ΠΊΠΈΡ
, ΡΡΠΎΠ²Π΅Π½Ρ ΠΠΠ ΠΎΠΊΠ°Π·Π°Π»ΡΡ Π² 1,5 ΡΠ°Π·Π° Π²ΡΡΠ΅ - 17,9% (12,3-28,2%) (p 0,05). Π 11,8% ΡΠ»ΡΡΠ°Π΅Π² Ρ Π»Π΅ΡΠ°Π»ΡΠ½ΡΠΌ ΠΈΡΡ
ΠΎΠ΄ΠΎΠΌ ΡΡΠΎΠ²Π΅Π½Ρ ΠΠΠ Π΄ΠΎΡΡΠΈΠ³Π°Π» 19% (16,5-26%) (p 0,05). ΠΠΎΠ½ΠΈΡΠΎΡΠΈΠ½Π³ ΡΡΠΎΠ²Π½Ρ ΠΠΠ Π² ΠΊΡΠΎΠ²ΠΈ 9 Π±ΠΎΠ»ΡΠ½ΡΡ
ΠΎΡ ΠΌΠΎΠΌΠ΅Π½ΡΠ° ΠΏΠΎΡΡΡΠΏΠ»Π΅Π½ΠΈΡ Π΄ΠΎ ΠΌΠΎΠΌΠ΅Π½ΡΠ° ΠΏΠ΅ΡΠ΅Π²ΠΎΠ΄Π° Π² ΠΎΡΠ΄Π΅Π»Π΅Π½ΠΈΠ΅ ΡΠ΅Π°Π½ΠΈΠΌΠ°ΡΠΈΠΈ ΠΈ ΠΈΠ½ΡΠ΅Π½ΡΠΈΠ²Π½ΠΎΠΉ ΡΠ΅ΡΠ°ΠΏΠΈΠΈ/Π²ΡΠΏΠΈΡΠΊΠΈ ΠΈΠ»ΠΈ ΡΠΌΠ΅ΡΡΠΈ ΠΏΠΎΠΊΠ°Π·Π°Π», ΡΡΠΎ ΡΠ½ΠΈΠΆΠ΅Π½ΠΈΠ΅ ΡΡΠΎΠ²Π½Ρ ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½Π½ΡΡ
ΠΠΠ ΠΎΡΡΠ°ΠΆΠ°Π΅Ρ ΡΠ»ΡΡΡΠ΅Π½ΠΈΠ΅ ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠΎΡΡΠΎΡΠ½ΠΈΡ ΠΈ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠΌΠΎΠΉ ΡΠ΅ΡΠ°ΠΏΠΈΠΈ. Π ΠΎΡΡ ΡΡΠΎΠ²Π½Ρ ΠΠΠ, Π½Π°ΠΏΡΠΎΡΠΈΠ², ΠΌΠΎΠΆΠ΅Ρ ΡΠ²ΠΈΠ΄Π΅ΡΠ΅Π»ΡΡΡΠ²ΠΎΠ²Π°ΡΡ ΠΎΠ± ΡΡ
ΡΠ΄ΡΠ΅Π½ΠΈΠΈ ΠΈ ΡΠΈΡΠΊΠ΅ Π½Π΅Π³Π°ΡΠΈΠ²Π½ΠΎΠ³ΠΎ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΠΎΠ±ΡΡΠΈΠΉ.ΠΠ°ΠΊΠ»ΡΡΠ΅Π½ΠΈΠ΅. ΠΡΡΠ²Π»Π΅Π½Ρ Π½Π΅ΠΊΠΎΡΠΎΡΡΠ΅ ΠΏΠ°ΡΠΎΡΠΈΠ·ΠΈΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΌΠ΅Ρ
Π°Π½ΠΈΠ·ΠΌΡ ΡΠ°Π·Π²ΠΈΡΠΈΡ COVID-19, ΡΠ²ΡΠ·Π°Π½Π½ΡΠ΅ Ρ ΠΊΠΎΠΌΠΏΠ°ΡΡΠΌΠ΅Π½ΡΠΎΠΌ Π½Π΅ΠΉΡΡΠΎΡΠΈΠ»ΠΎΠ². Π£ΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΎ, ΡΡΠΎ Π΄Π»Ρ Π±ΠΎΠ»ΡΠ½ΡΡ
Ρ ΠΊΠΎΡΠΎΠ½Π°Π²ΠΈΡΡΡΠ½ΠΎΠΉ ΠΈΠ½ΡΠ΅ΠΊΡΠΈΠ΅ΠΉ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠ½ΠΎ Π½Π°Π»ΠΈΡΠΈΠ΅ Π²ΡΡΠΎΠΊΠΎΠ³ΠΎ ΡΡΠΎΠ²Π½Ρ ΠΠΠ, ΠΊΠΎΡΠΎΡΡΠΉ Π² 3 ΡΠ°Π·Π° ΠΈ Π±ΠΎΠ»Π΅Π΅ ΠΏΡΠ΅Π²ΡΡΠ°Π΅Ρ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΠΈ ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈ Π·Π΄ΠΎΡΠΎΠ²ΡΡ
Π΄ΠΎΠ±ΡΠΎΠ²ΠΎΠ»ΡΡΠ΅Π² ΠΈ ΡΠ²ΠΈΠ΄Π΅ΡΠ΅Π»ΡΡΡΠ²ΡΠ΅Ρ ΠΎ ΡΠ±ΠΎΠ΅ ΠΈΠΌΠΌΡΠ½Π½ΡΡ
ΠΌΠ΅Ρ
Π°Π½ΠΈΠ·ΠΌΠΎΠ² Π·Π°ΡΠΈΡΡ ΠΈ ΡΠ°Π·Π²ΠΈΡΠΈΠΈ Π½Π΅Π°Π΄Π΅ΠΊΠ²Π°ΡΠ½ΠΎΠ³ΠΎ Π²ΠΎΡΠΏΠ°Π»ΠΈΡΠ΅Π»ΡΠ½ΠΎΠ³ΠΎ ΠΎΡΠ²Π΅ΡΠ°. ΠΠΎΠ²ΡΡΠ΅Π½ΠΈΠ΅ Π½Π΅ΠΉΡΡΠΎΡΠΈΠ»ΡΠ½ΡΡ
Π²Π½Π΅ΠΊΠ»Π΅ΡΠΎΡΠ½ΡΡ
Π»ΠΎΠ²ΡΡΠ΅ΠΊ Π² ΠΌΠ°Π·ΠΊΠ°Ρ
ΡΠ΅Π»ΡΠ½ΠΎΠΉ ΠΊΡΠΎΠ²ΠΈ Π±ΠΎΠ»Π΅Π΅ 16% ΠΌΠΎΠΆΠ΅Ρ ΡΠ»ΡΠΆΠΈΡΡ ΠΊΡΠΈΡΠ΅ΡΠΈΠ΅ΠΌ Π½Π΅Π³Π°ΡΠΈΠ²Π½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ³Π½ΠΎΠ·Π° ΡΠ΅ΡΠ΅Π½ΠΈΡ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΡ ΠΈ ΡΠΈΡΠΊΠ° Π»Π΅ΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΈΡΡ
ΠΎΠ΄Π°
Causes of Juvenile Delinquency in the Republic of Kazakhstan
Goal: The goal of this paper is to identify the causes of juvenile delinquency in the Republic of Kazakhstan. Methodology: Application of interviewing and expert estimation methods, as well as sociological method helped to study crime among minors as a periodically changing social and legal phenomenon caused by a set of social, political, economic, legal and psychological-pedagogical reasons and conditions. Results: The primary causes of juvenile delinquency in the Republic of Kazakhstan have been identified. These include: impact of the mass media, Internet etc., which resulted in a weakening of the previously existing family and social traditions; low social level of the family; asocial lifestyle of parents; lack of control by adults; negative environment in which a juvenile lives and resides; inoccupation and lack of leisure during non-study time. Β© 2020, Springer Nature Switzerland AG
Climate and biotic evolution during the Permian-Triassic transition in the temperate Northern Hemisphere, Kuznetsk Basin, Siberia, Russia
The Siberian Traps volcanism is widely considered the main cause of the end-Permian mass extinction, the greatest biological crisis in the Earth history. While the extinction is interpreted as catastrophic and sudden with estimates of duration of approximately 35β40 thousand years from marine strata in South China, various lines of evidence have emerged for a more complex, prolonged, and diachronous extinction pattern. We present here the results of a multidisciplinary study of the Permian-Triassic continental transition in the Kuznetsk Basin, Russia. The region is proximal to the Siberian Traps LIP and the detrimental effects of the flood basalt volcanism in the Kuznetsk Basin may have been of similar scale as in the main area of the Siberian Traps distribution (Tunguska and Taymyr regions). Whereas earlier work has placed the Permian-Triassic boundary position between the coal-bearing Tailugan Formation and the volcanoclastic Maltsev Formation, here we revised the traditional model using three independent methods: radioisotopic CA-IDTIMS U-Pb zircon ages, Ξ΄13Corg isotope values and paleomagnetic proxies. The regional extinction of the humid-dominated forest flora (cordaites) and the aridity-induced biotic turnover in the Kuznetsk Basin occurred 820 kyr earlier than the end-Permian extinction event recorded in South China at 251.94 Ma. The biota in Kuznetsk Basin at the turnover subsequently diversified (with some exceptions) across the Permian-Triassic transition. By compiling a large taxonomic database, we find that marine and terrestrial biotic diversity in Siberia progressively increased from the beginning of the Permian up to the middle Roadian (early Guadalupian global glacial event). After that time, the diversity at the species and generic level progressively and slowly declined towards the aforementioned latest Changhsingian (252.76 Ma) biotic turnover. Starting from this time, the biota rapidly diversified in the latest Changhsingian and Early-Middle Triassic. We suggest that the Permian-Triassic mass extinction mostly occurred in the tropics and subtropics due to the strong climatic warming, which was relatively low in late Changhsingian and gradually but quickly extends in the latest Changhsingian to an abnormally high temperature and extremely low oxygenated water in the oceans that was deadly for most marine animals. The warm climate shift poleward during Permian-Triassic transition in the middle-high latitudes caused the replacement (turnover) of the humid-related biotas by the dry climate-related and more diverse communities, which continued to expand throughout the Triassic in both marine and terrestrial habitats. The pattern of the Permian-Triassic event in both marine and terrestrial habitats was more intricate in terms of extinction, turnover, and diversity of biota within the different climatic zones and environmental habitats than has been generally considered